The goal of the SBS 2015 Suggestion Track is to evaluate approaches for supporting users in searching collections of books who express their information needs both in a query and through example books. The track investigates the complex nature of relevance in book search and the role of traditional and user-generated book metadata in retrieval. We extended last year's investigation into the nature of book suggestions from the LibraryThing forums and how they compare to book relevance judgements. Participants were encouraged to incorporate rich user pro les of both topic creators and other LibraryThing users to explore the relative value of recommendation and retrieval paradigms for book search. We found further support that such suggestions are a valuable alternative to traditional test collections that are based on top-k pooling and editorial relevance judgements. In terms of systems evaluation, the most e ective systems include some form of learning-to-rank. It seems that the complex nature of the requests and the book descriptions, with multiple sources of evidence, requires a careful balancing of system parameters.
The goal of the Social Book Search 2015 Suggestion Track3 is to investigate techniques to support users in searching for books in catalogues of professional metadata and complementary social media. Towards this goal the track is building appropriate evaluation benchmarks, complete with test collections for social, semantic and focused search tasks. The track provides opportunities to explore research questions around two key areas:
The Social Book Search (SBS) 2015 Suggestion Track, framed within the scenario of a user searching a large online book catalogue for a given topic of interest, aims at exploring techniques to deal with complex information needs| that go beyond topical relevance and can include aspects such as genre, recency, engagement, interestingness, and quality of writing|and complex information sources that include user pro les, personal catalogues, and book descriptions containing both professional metadata and user-generated content.
The 2015 Suggestion Track is a continuation of the INEX SBS Track that ran from 2011 up to 2014. For this fth edition the focus is on search requests that combine a natural language description of the information need as well as example books, combining traditional ad hoc retrieval with query-by-document. The information needs are derived from the LibraryThing (LT) discussion forums. LibraryThing forum requests for book suggestions, combined with annotation of these requests resulted in a topic set of 208 topics with graded relevance judgments. A test collection is constructed around these information needs and the Amazon/LibraryThing collection, consisting of 2.8 million documents. The Suggestion Track runs in close collaboration with the SBS Interactive Track,4 which is a user-centered track where interfaces are developed and evaluated and user interaction is analysed to investigate how book searchers make use of professional metadata and user-generated content.
In this paper, we report on the setup and the results of the 2015 Suggestions Track as part of the SBS Lab at clef 2015. First, in Section 2, we give a brief summary of the participating organisations. The SBS task itself is described in Section 3. Sections 4 and 5 describe the test collection and the evaluation process in more detail. We close in Section 6 with a summary and plans for 2016. 2
A total of 25 organisations registered for the track
The goal of the Social Book Search (SBS) track is to evaluate the value of professional metadata and user-generated content for book search on the Web and to develop and evaluate systems that can deal with both retrieval and recommendation aspects, where the user has a speci c information need against a background of personal tastes, interests and previously seen books.
Through social media, book descriptions have extended far beyond what is
traditionally stored in professional catalogues. Not only are books described in
the users' own vocabulary, but are also reviewed and discussed online, and added
4 See http://social-book-search.humanities.uva.nl/#/interactive
to online personal catalogues of individual readers. This additional information
is subjective and personal, and opens up opportunities to aid users in searching
for books in di erent ways that go beyond the traditional editorial metadata
based search scenarios, such as known-item and subject search. For example,
readers use many more aspects of books to help them decide which book to read
next
The SBS track investigates book requests and suggestions from the LibraryThing (LT) discussion forums as a way to model book search in a social environment. The discussions in these forums show that readers frequently turn to others to get recommendations and tap into the collective knowledge of a group of readers interested in the same topic.
The track builds on the INEX Amazon/LibraryThing (A/LT) collection
The SBS Suggestion Track aims to address the following research questions: { Can we build reliable and reusable test collections for social book search based on book requests and suggestions from the LT discussion forums? { Can user pro les provide a good source of information to capture personal, a ective aspects of book search information needs? { How can systems incorporate both speci c information needs and general user pro les to combine the retrieval and recommendation aspects of social book search? { What is the relative value of social and controlled book metadata for book search? 3.2
The scenario is that of a user turning to Amazon Books and LT to nd books to read, to buy or to add to their personal catalogue. Both services host large collaborative book catalogues that may be used to locate books of interest.
On LT, users can catalogue the books they read, manually index them by assigning tags, and write reviews for others to read. Users can also post messages on discussion forums asking for help in nding new, fun, interesting, or relevant books to read. The forums allow users to tap into the collective bibliographic knowledge of hundreds of thousands of book enthusiasts. On Amazon, users can read and write book reviews and browse to similar books based on links such as \customers who bought this book also bought... ".
Users can search online book collections with di erent intentions. They can search for speci c known books with the intention of obtaining them (buy, download, print). Such needs are addressed by standard book search services as o ered by Amazon, LT and other online bookshops as well as traditional libraries. In other cases, users search for a speci c, but unknown, book with the intention of identifying it. Another possibility is that users are not looking for a speci c book, but hope to discover one or more books meeting some criteria. These criteria can be related to subject, author, genre, edition, work, series or some other aspect, but also more serendipitously, such as books that merely look interesting or fun to read or that are similar to a previously read book. 3.3
The task is to reply to a user request posted on a LT forum (see Section 4.1) by returning a list of recommended books matching the user's information need. More speci cally, the task assumes a user who issues a query to a retrieval system, which then returns a (ranked) list of relevant book records. The user is assumed to inspect the results list starting from the top, working down the list until the information need has been satis ed or until the user gives up. The retrieval system is expected to order the search results by relevance to the user's information need.
The user's query can be a number of keywords, but also one or more book records as positive or negative examples. In addition, the user has a personal pro le that may contain information on the user's interests, list of read books and connections with other readers. User requests may vary from asking for books on a particular genre, looking for books on a particular topic or period or books written in a certain style. The level of detail also varies, from a brief statement to detailed descriptions of what the user is looking for. Some requests include examples of the kinds of books that are sought by the user, asking for similar books. Other requests list examples of known books that are related to the topic, but are speci cally of no interest. The challenge is to develop a retrieval method that can cope with such diverse requests.
The books must be selected from a corpus that consists of a collection of curated and social book metadata, extracted from Amazon Books and LT, extended with associated records from library catalogues of the Library of Congress and the British Library (see the next section). Participants of the Suggestion track are provided with a set of book search requests and user pro les and are asked to submit the results returned by their systems as ranked lists.
The track thus combines aspects from retrieval and recommendation. On the one hand the task is akin to directed search familiar from information retrieval, with the requirement that returned books should be topically relevant to the user's information need described in the forum thread. On the other hand, users may have particular preferences for writing style, reading level, knowledge level, novelty, unusualness, presence of humorous elements and possibly many other aspects. These preferences are to some extent re ected by the user's reading pro le, represented by the user's personal catalogue. This catalogue contains the books already read or earmarked for future reading, and may contain personally assigned tags and ratings. Such preferences and pro les are typical in recommendation tasks, where the user has no speci c information need, but is looking for suggestions of new items based on previous preferences and history. 3.4
2015 Suggestion Task This year, the task focuses on search requests that combine a rich narrative description of the information need and one or more example books that the requester considers positive or negative. The challenge for systems is to nd the right balance between the two types of evidence and how to use the natural language statement to infer the relevant aspects of the example books. 3.5
Participants are asked to return a ranked list of books for each user query, ranked by order of relevance, where the query is described in the LT forum thread. We adopt the submission format of TREC, with a separate line for each retrieval result (i.e., book), consisting of six columns: 1. topic id: the topic number, which is based on the LT forum thread number. 2. Q0: the query number. Unused, so should always be Q0. 3. isbn: the ISBN of the book, which corresponds to the le name of the book description. 4. rank: the rank at which the document is retrieved. 5. rsv: retrieval status value, in the form of a score. For evaluation, results are ordered by descending score. 6. run id: a code to identify the participating group and the run.
Participants are allowed to submit up to six runs, of which at least one should use only the title eld of the topic statements (the topic format is described in Section 4.1). For the other ve runs, participants could use any eld in the topic statement. 4
We use and extend the Amazon/LibraryThing (A/LT) corpus crawled by the
University of Duisburg-Essen for the INEX Interactive Track
The collection consists of 2.8 million book records from Amazon, extended with social metadata from LT. This set represents the books available through Amazon. The records contain title information as well as a Dewey Decimal Classi cation (DDC) code (for 61% of the books) and category and subject information supplied by Amazon. We note that for a sample of Amazon records the subject descriptors are noisy, with a number of inappropriately assigned descriptors that seem unrelated to the books.
Each book is identi ed by an ISBN. Note that since di erent editions of the same work have di erent ISBNs, there can be multiple records for a single intellectual work. Each book record is an XML le with elds like isbn, title, author, publisher, dimensions, numberofpages and publicationdate. Curated metadata comes in the form of a Dewey Decimal Classi cation in the dewey eld, Amazon subject headings in the subject eld, and Amazon category labels in the browseNode elds. The social metadata from Amazon and LT is stored in the tag, rating, and review elds. The full list of elds is shown in Table 2.
To ensure that there is enough high-quality metadata from traditional library catalogues, we extended the A/LT data set with library catalogue records from the Library of Congress (LoC) and the British Library (BL). We only use library records of ISBNs that are already in the A/LT collection. These records contain formal metadata such as title information (book title, author, publisher, etc.), classi cation codes (mainly DDC and LCC) and rich subject headings based on the Library of Congress Subject Headings (LCSH).5 Both the LoC records and the BL records are in MARCXML6 format. There are 1,248,816 records from the LoC and 1,158,070 records in MARC format from the BL. Combined, there are 5 For more information see: http://www.loc.gov/aba/cataloging/subject/ 6 MARCXML is an XML version of the well-known MARC format. See: http://www. loc.gov/standards/marcxml/ 2,406,886 records covering 1,823,998 of the ISBNs in the A/LT collection (66%). Although there is no single library catalogue that covers all books available on Amazon, we reason that these combined library catalogues can improve both the quality and quantity of professional book metadata. Indeed, with the LoC and BL data sets combined, 79% of all ISBNs in the original A/LT corpus now have a DDC code. In addition, the LoC data set also has LCC codes for 44% of the records in the collection. With only the A/LT data, 57% of the book descriptions have at least one subject heading, but with the BL and LoC data added, this increases to 80%. Furthermore, the A/LT data often has only a single subject heading per book, whereas in the BL and LoC data sets, book descriptions typically have 2{4 headings (average 2.96). Thus, the BL and LoC data sets increase the coverage of curated metadata, such that the vast majority of descriptions in our data set include professionally assigned classi cation codes and subject headings. 4.1
LT users discuss their books on the discussion forums. Many of the topic threads are started with a request from a member for interesting, fun new books to read. Users typically describe what they are looking for, give examples of what they like and do not like, indicate which books they already know and ask other members for recommendations. Members often reply with links to works catalogued on LT, which, in turn, have direct links to the corresponding records on Amazon. These requests for recommendations are natural expressions of information needs for a large collection of online book records. We use a sample of these forum topics to evaluate systems participating in the Suggestion Track.
Each topic has a title and is associated with a group on the discussion forums. For instance, topic 99309 in Figure 1 has the title Politics of Multiculturalism Recommendations? and was posted in the group Political Philosophy. The books suggested by members in the thread are collected in a list on the side of the topic thread (see Figure 1). A feature called touchstone can be used by members to easily identify books they mention in the topic thread, giving other readers of the thread direct access to a book record in LT, with associated ISBNs and links to Amazon. We use these suggested books as initial relevance judgements for evaluation. In the rest of this paper, we use the term suggestion to refer to a book that has been identi ed in a touchstone list for a given forum topic. Since all suggestions are made by forum members, we assume they are valuable judgements on the relevance of books. Additional relevance information can be gleaned from the discussions on the threads. Consider, for example, topic 1299397. The topic starter rst explains what sort of books he is looking for, and which relevant books he has already read or is reading. Other members post responses with book suggestions. The topic starter posts a reply describing which suggestions he likes and which books he has ordered and plans to read. Later on, the topic starter provides feedback on the suggested books that he has now read. Such feedback can be used to estimate the relevance of a suggestion to the user.
In the following, we rst describe the topic selection and annotation procedure, then how we used the annotations to assign relevance values to the
suggestions, and nally the user pro les, which were then provided with each topic.
Topic selection The topic set of 2015 is a subset of the 2014 topic set, focusing on topics with both a narrative description of the information need and one or more example books to guide the suggestions.
In 2013 and 2014, we had a group of eight di erent Information Science
students annotate the narratives of a random sample of 2,646 LT forum
topics
Each topic has at least one example book provided by the requester that helps other forum members understand in which direction the requester is thinking. The number of examples ranges from 1 to 21 (Table 3), with a median and mean of 2 and 2.48 respectively. Further, annotators indicated whether an example book was given as a positive example|i.e. they are looking for something along the lines of the example|or as a negative example, where the example is broadly relevant but has aspects that the requester does not want in the suggested books.
After annotation, the topic in Figure 1 (topic 99309) is distributed to participants in the following format: <topic id="99309"> <query>Politics of Multiculturalism</query> <title>Politics of Multiculturalism Recommendations?</title> <group>Political Philosophy</group> <narrative> I'm new, and would appreciate any recommended reading on the politics of multiculturalism. <a href="/author/parekh">Parekh </a>'s <a href="/work/164382">Rethinking Multiculturalism: Cultural Diversity and Political Theory</a> (which I just finished) in the end left me unconvinced, though I did find much of value I thought he depended way too much on being able to talk out the details later. It
The hyperlink markup, represented by the <a> tags, is added by the Touchstone technology of LT. The rest of the markup is generated speci cally for the Suggestion Track. Above, the example book with LT id 164382 is annotated as an example that the requester is neutral about. It has positive and negative aspects. From the request, forum members can understand how to interpret this example.
Finally, annotators had to label each touchstone provided by LT members (including any provided by the topic starter). They had to indicate whether the suggester has read the book. For the has read question, the possible answers were Yes, No, Can't tell and It seems like this is not a book. They also had to judge the attitude of the suggester towards the book. Possible answers were Positively, Neutrally, Negatively, Not sure or This book is not mentioned as a relevant suggestion! The latter can be chosen when someone mentions a book for another reason than to suggest it as a relevant book for the topic of request.
In the majority of cases (61%) members suggested books that they have read. It is rather rare for suggesters to state that they have not read a suggested book (8%). More often, suggesters do not reveal whether they have read the book or not (28%). Books mentioned in response to a book search request are often presented in a positive (47%) or neutral (39%) way. Both positive and negative suggestions tend to come from members who have read the books (71% and 87% respectively). When books are mentioned in a neutral way, it is often di cult to tell whether the book has been read by the suggester, although a third of the neutral mentions comes from members who have read the book.
All in all, in response to a book search request members suggest mostly books they have read and often in a positive way. This supports our choice of using forum suggestions as relevance judgements.
In addition to the explicitly marked up books, e.g., the examples and suggestions, we noticed that there are other book titles that are not marked up but are intended as suggestions. In some cases this is because the suggester is not aware of the Touchstone syntax or because it fails to identify the correct book and they cannot manually correct it. To investigate the extent of this issue and to make the list of identi ed suggestions more complete, in 2015 we manually labeled all suggested books that were not marked up by Touchstone in each forum thread of the 208 topics.
This resulted in 830 new suggestions (a mean of 4 per topic). From the touchstones we extracted 4240 suggestions (20.4 per topic), so the manually extracted suggestions bring the total to 5070 (24.4), an increase of 20%. Multiple user may suggest the same books, so the total number of suggested books is lower. The 4240 touchstone suggestion represent 3255 books (15.6 per topic). With the manually extracted suggestions, this increases to 3687 (17.7 per topic), an increase of 13%. The newly added suggestions therefore increase the recall base but also increase the number of recommendations for some of the touchstone suggestions.
tated suggestions to relevance judgements uses the same process as in 2014. Note that some of the books mentioned in the forums are not part of the 2.8 million books in our collection. These suggestions removed from the suggestions any books that are not in the INEX A/LT collection. The numbers reported in the previous section were calculated after this ltering step.
Forum members can mention books for many di erent reasons. We want the relevance values to distinguish between books that were mentioned as positive recommendations, negative recommendations (books to avoid), neutral suggestions (mentioned as possibly relevant but not necessarily recommended) and books mentioned for some other reason (not relevant at all). We also want to di erentiate between recommendations from members who have read the book they recommend and members who have not. We assume a recommendation to be of more value to the searcher if it comes from someone who has actually read the book. For the mapping to relevance values, we refer to the rst mention of work as the suggestion and subsequent mentions of the same work as replies. We use has read when the forum members have read the book they mention and not read when they have not. Furthermore, we use a number of simplifying assumptions: { When the annotator was not sure if the person mentioning a book has read it, we treat it as not read. We argue that for the topic starter there is no clear di erence in the value of such recommendations. { When the annotator was not sure if a suggestion was positive, negative or neutral, we treat it as neutral. Again, for the topic starter there is no clear signal that there is di erence in value. { A work with only negative suggestions has no value for the requester when found in the search results. { has read recommendations overrule not read recommendations. Someone who has read the book is in a better position to judge a book than someone who has not. { positive and negative recommendations neutralise each other. I.e. a positive and a negative recommendation together are the same as two neutral recommendations. { If the topic starter has read a book she mentions, the relevance value is rv = 0. We assume such books have no value as suggestions. { The attitude of the topic starter towards a book overrules those of others.
The system should retrieve books for the topic starter, not for others. { When a single forum member mentions a single work multiple times, we use the last mention as judgement.
With the following decision tree we determine from which forum members want to use the judgements to derive relevance values: 1. Book mentioned by single member ! use that member's judgement 2. Book mentioned by multiple members 2.1 topic starter mentions book 2.1.1 topic starter only suggests neutrally ! use replies of others (2.2) 2.1.1 topic starter suggests positively/negatively ! use starter judgement 2.1.1 topic starter replies ! use starter judgement 2.2 topic starter does not mention book 2.2.2 members who have read the book suggest/reply ! use has read judgements 2.2.2 no member who suggests/replies about a book has read it ! use all judgements Once the judgements per suggested book are determined, we map the annotated judgements to relevance values. To determine what relevance values to use, we observe that there are positive, neutral and negative suggestions by one or multiple suggesters. Based on the simplifying assumption that a work that is only mentioned negatively has no value for the suggester when found in the search results (rv = 0), we expect that works with more negative than positive suggestions have at least some value, but less than works with on average either neutral suggestions or positive suggestions. Therefore, a work with on average negative suggestions has the lowest positive relevance value rv = 1. On average neutral suggestions are the next level, with rv = 2. Works with on average positive suggestions get a relevance value higher than two, with a single positive suggestion or a mix of positive and negative suggestion getting an additional relevance point (rv = 3) and multiple positive suggestions two additional points (rv = 4). If the judges have read the books, the additional relevance points are multiplied by two because they represent more reliable judgements. Speci cally, the values are assigned according to the following scheme: 1. catalogued by topic creator 1.1 post-catalogued ! rv = 8 1.2 pre-catalogued ! rv = 0 2. single judgement 2.1 starter has read judgement ! rv = 0 2.2 starter has not read judgement 2.2.2 starter positive ! rv = 8 2.2.2 starter neutral ! rv = 2 2.2.2 starter negative ! rv = 0 2.3 other member has read judgement 2.3.3 has read positive ! rv = 4 2.3.3 has read neutral ! rv = 2 2.3.3 has read negative ! rv = 0 2.4 other member has not read judgement 2.4.4 not read positive ! rv = 3 2.4.4 not read neutral ! rv = 2 2.4.4 not read negative ! rv = 0 3. multiple judgements 3.1 multiple has read judgements 3.1.1 some positive, no negative ! rv = 6 3.1.1 #positive > #negative ! rv = 4 3.1.1 #positive == #negative ! rv = 2 3.1.1 all neutral ! rv=2 3.1.1 #positive < #negative ! rv = 1 3.1.1 no positive, some negative ! rv = 0 3.2 multiple not read judgements 3.2.2 some positive, no negative ! rv = 4 3.2.2 #positive > #negative ! rv = 3 3.2.2 #positive == #negative ! rv = 2 3.2.2 all neutral ! rv=2 3.2.2 #positive < #negative ! rv = 1 3.2.2 no positive, some negative ! rv = 0 This results in graded relevance values with seven possible values (0, 1, 2, 3, 4, 6, 8).
User pro les and personal catalogues From LT we can not only extract the information needs of social book search topics, but also the rich user pro les of the topic creators and other LT users, which contain information on which books they have in their personal catalogue on LT, which ratings and tags they assigned to them and a social network of friendship relations, interesting library relations and group memberships. These pro les may provide important signals on the user's topical and genre interests, reading level, which books they already know Type Topic Creators
Pre-catalogued Post-catalogued
Total catalogue All users
Others Total
N 208 208 208 135,722 74,240 209,962 93,976 33,503,999 94,656 34,112,435 total min
max median mean stdev 1 5884 1 5619 2 6272 1 41,792 1 41,792 270 653 991 146 357 587 541 1009 1275 134 135 357 704 360 710 and which ones they like and don't like. These pro les were scraped from the LT site, anonymised and made available to participants. This allows Track participants to experiment with combinations of retrieval and recommender systems. One of the research questions of the SBS task is whether this pro le information can help systems in identifying good suggestions.
Although the user expresses her information need in some detail in the discussion forum, she may not describe all aspects she takes into consideration when selecting books. This may partly be because she wants to explore di erent options along di erent dimensions and therefore leaves some room for di erent interpretations of her need. Another reason might be that some aspects are not related directly to the topic at hand but may be latent factors that she takes into account with selecting books in general.
To anonymise all user pro les, we rst removed all friendship and group membership connections and replaced the user name with a randomly generated string. The cataloguing date of each book was reduced to the year and month. What is left is an anonymised user name, book ID, month of cataloguing, rating and tags.
Basic statistics on the number of books per user pro le is given in Table 4. By the time users ask for book recommendations, most of them already have a substantial catalogue (pre-catalogued). The distribution is skewed, as the mean (653) is higher than the median (270). After posting their topics, users tend to add many more books (post-catalogued), but fewer than they have already added. Compared to the other users in our crawl (median of 135 books), the topic creators are the more active users, with larger catalogues (median of 541 books).
ISBNs and Intellectual Works Each record in the collection corresponds to an ISBN, and each ISBN corresponds to a particular intellectual work. An intellectual work can have di erent editions, each with their own ISBN. The ISBN-to-work relation is a many-to-one relation. In many cases, we assume the user is not interested in all the di erent editions, but in di erent intellectual works. For evaluation we collapse multiple ISBN to a single work. The highest ranked ISBN is evaluated and all lower ranked ISBNs of the same work ignored. Although some of the topics on LibraryThing are requests to recommend a particular edition of a work|in which case the distinction between di erent ISBNs for the same work are important|we ignore these distinctions to make evaluation easier. This turns edition-related topics into known-item topics.
However, one problem remains. Mapping ISBNs of di erent editions to a single work is not trivial. Di erent editions may have di erent titles and even have di erent authors (some editions have a foreword by another author, or a translator, while others have not), so detecting which ISBNs actually represent the same work is a challenge. We solve this problem by using mappings made by the collective work of LibraryThing members. LT members can indicate that two books with di erent ISBNs are actually di erent manifestations of the same intellectual work. Each intellectual work on LibraryThing has a unique work ID, and the mappings from ISBNs to work IDs is made available by LibraryThing.8
The mappings are not complete and might contain errors. Furthermore, the mappings form a many-to-many relationship, as two people with the same edition of a book might independently create a new book page, each with a unique work ID. It takes time for members to discover such cases and merge the two work IDs, which means that at any time, some ISBNs map to multiple work IDs even though they represent the same intellectual work. LibraryThing can detect such cases but, to avoid making mistakes, leaves it to members to merge them. The fraction of works with multiple ISBNs is small so we expect this problem to have a negligible impact on evaluation. 5
This year, 11 teams submitted a total of 48 automatic runs (see Table 1) and one manual run. We omit the manual run, as it is a ranking of last year's Qrels. The o cial evaluation measure for this task is nDCG@10. It takes graded relevance values into account and is designed for evaluation based on the top retrieved results. In addition, P@10, MAP and MRR scores will also be reported, with the evaluation results shown in Table 5.
The best runs of the top 5 groups are described below: 1. MIIB - Run6 (rank 1): For this run, queries are generated from all topic elds and applied on a BM25 index with all textual document elds merged into a single eld. A Learning-to-rank framework is applied using random forest on 6 result lists as well as the price, the book length and the ratings.
Results are re-ranked based on tags and ratings. 2. CERIST - CERIST TOPICS EXP NO (rank 2): The terms of topics have been combined with the top tags extracted from the example books mentioned in the book search request then the BM15 model has been used to rank books. The books which have been catalogued by the users have been removed.
mrr map
Pro les
Most of the top performing systems, including the best (MIIB's Run6) make no use of user pro le information. There are 11 systems that made use of the user pro les, with 4 in the top 10 (at ranks 2, 4, 6 and 9). So far, the additional value of user pro les has not been established. The best systems combine various topic elds, with parameters trained for optimal performance. Several of the best performing systems make use of learning-to-rank approaches, suggesting book search is a domain where systems need to learn from user behaviour what the right balance is for the multiple and diverse sources of information, both from the collection and the user side. 6
This was the rst year of the SBS Suggestion Track, which is a continuation from the SBS Track at INEX 2011{2014. The overall goal remains to investigate the relative value of professional metadata, user-generated content and user pro les, but the speci c focus for this year is to construct a test collection to evaluate systems dealing with complex book search requests that combine an information need expressed in a natural language statement and through example books. The number of active participants increased to 11, suggesting this speci c focus of interest in the IR community.
Extended the setup of the previous year, we kept the evaluation procedure the same, but included manually extracted suggestions from the LT forum threads that were not explicitly marked up by forum members. In addition, we added annotated example books with each topic statement, so that participants can investigate the value of query-by-example techniques in combination with more traditional text-based queries.
We found that the manually extracted suggestions increase the recall base but also further skew the distribution of suggestions, with more books receiving multiple suggestions, thereby increasing their relevance value.
The evaluation has shown that the most e ective systems either adopt a learning-to-rank approach or incorporate keywords from the example books in the textual query. The e ectiveness of learning-to-rank approaches suggests the complexity of dealing with multiple sources of evidence|book descriptions by multiple authors, di ering in nature from controlled vocabulary descriptors, freetext tags and full-text reviews and information needs and interests represented by both natural language statements and user pro les|requires optimizing parameters through observing users' interactions.
Next year, we continue this focus on complex topics with example books and consider including an recommender systems type evaluation. We are also thinking of a pilot task in which the system not only has to retrieve relevant and recommendable books, but also to select which part of the book description| e.g. a certain set of reviews or tags|is most useful to show to the user, given her information need.