=Paper=
{{Paper
|id=Vol-1609/16091183
|storemode=property
|title=KNOW At The Social Book Search Lab 2016 Suggestion Track
|pdfUrl=https://ceur-ws.org/Vol-1609/16091183.pdf
|volume=Vol-1609
|authors=Hermann Ziak,Roman Kern
|dblpUrl=https://dblp.org/rec/conf/clef/ZiakK16
}}
==KNOW At The Social Book Search Lab 2016 Suggestion Track==
https://ceur-ws.org/Vol-1609/16091183.pdf
KNOW At The Social Book Search Lab 2016
Suggestion Track
Hermann Ziak and Roman Kern
Know-Center GmbH
Inffeldgasse 13
8010 Graz, Austria
hziak, rkern@know-center.at
Abstract. Within this work represents the documentation of our ap-
proach on the Social Book Search Lab 2016 where we took part in the
suggestion track. The main goal of the track was to create book recom-
mendation for readers only based on their stated request within a forum.
The forum entry contained further contextual information, like the user’s
catalogue of already read books and the list of example books mentioned
in the user’s request. The presented approach is mainly based on the
metadata included in the book catalogue provided by the organizers of
the task. With the help of a dedicated search index we extracted several
potential book recommendations which were re-ranked by the use of an
SVD based approach. Although our results did not meet our expectation
we consider it as first iteration towards a competitive solution.
Keywords: SVD, recommender engine, content-based information retrieval
1 Introduction
The Social Book Search (SBS) Lab 2016 has the objective to investigate book
search in a setting where the user is not inferring an actual query into a search
engine. The setting can more be considered as a recommender system auto-
matically inferring the information need of the user by its context. Therefore
the organizers prepared three different tracks the ’interactive’ track, the ’min-
ing’ track and the ’suggestion’ track. This paper represents the approach and
the results of our participation in the ’suggestion’ track. Here the task was the
extraction of the user’s information need within a posting of the user on the
’LibraryThing’1 . According to this initial post of the user the final goal was to
suggest a ranked list of books, in that regard LibraryThing ’work IDs’, out of
a provided catalogue of books. The by the organizers supplied data contained
a feature rich dataset of the posting it self, according metadata of the user’s
history and in some cases examples in terms of mentioned book titles.
1
www.librarything.com
This catalogue contained a collection of about 2.7 million crawled records of
the ’Amazon.com’2 platform [2] combined with the information for the equiva-
lent work available on ’LibraryThing’ jointed into structured XML files.
Within the field of recommender systems there is a vast amount of sophisticated
approaches to tackle such problems [1]. Basically, all those approaches fall within
three different categories: content-based, collaborative, and hybrid approaches.
For our first attempt to contribute to the SBS Lab we decided to use the, from
our perspective least complex, content-based approach. Further, BM25 based ap-
proaches accomplished good results in recent years within this lab [3]. Therefore
the book collection was indexed with all the according metadata in a Apache
Lucene 3 based search engine. With the help of the mentioned search engine we
implemented an approach basically just relying on the use of the tags and browse
nodes provided within this dataset. Although the achieved results are below our
expectations we consider it as first step towards a competitive approach.
2 Approach
The main idea of our approach was to rely on the provided metadata, in that
regard the tags (T), browse nodes (BN) and ISBNs, of the provided book cata-
logue. Those fields contain textual content that categorize the book. (e.g. Sci-fi,
Novel, Child’s book)
The provided user postings consisted of several fields: I) the name of the
group where the request was initially posted (e.g. ’Sci-Fi Novels’), II) the title
of the entry, III) the actual request in form of natural language, IV) potential
book examples, V) the catalogue of already read books of the user. An example
of such posting can be found in Figure 1.
As initial step provided ISBN numbers of the example books and the users
catalogue books were sent to the search engine to get an initial dataset of T and
BNs. To this set of T and BNs weights were assigned based on a heuristic. Ts
or BNs that were contained within the examples of the according request were
considered to be more important and therefore got higher weights assigned than
ones just appearing within the catalogue of the user. The weight was further
increased if the tag was contained within the provided posting, title or group.
The outcome of this first steps was to separated set, one containing only the
weighted tags, one containing only the weighted browse nodes. Out of this two
lists two queries were formulated. With those queries we only search within the
tags or the browse nodes fields. The outcome of this step were two groups of
potential book candidates. To remove duplicates and already read books by the
user both candidate lists were filtered by the books already mentioned within
the user’s catalogue or the example books. To consolidate and re-rank this two
candidate lists we applied a SVD approach which are frequently used within
recommender systems [4,5] Both sets were transformed into one utility matrix
where the rows represented all the documents. The columns represented all T
2
www.amazon.com
3
https://lucene.apache.org/
Fig. 1. Example of a user’s suggestion request
and BN within both candidate lists. As value within the resulting matrix 1 was
set if T or BN was actually represented within the actual document or 0 if
not. Finally the resulting matrix was decomposed. Further, we created a vector
out of all the tags corresponding to the columns of the matrix was created. As
values for this tags the initial applied weights were set that were generated in
the third processing step according to Figure 2. If the tag was not existing in the
documents of the examples or catalogue the values was set to zero. To get the
final candidate ranking we applied this vector to the decomposed matrix. The
final outcome of the whole process was the ranked list of documents which we
could map to ISBN numbers. In a last step this ISBN numbers were translated
into the LibraryThing work IDs. This detour over the ISBN numbers was only
necessary since we had to report work IDs in the task and the Amazon dataset
only contained the ISBN numbers. The whole process is visualized in Figure 2.
3 Results
Table 1 shows the results of one of our conducted pre-tests. In this particular
case, we only submitted a small catalogue of books to the system to get similar
books recommended.
Table 1. One of the pre-test conducted to initially validate the approach. Within
the catalogue row the book titles that were used as input are stated. The result row
contains the titles returned by the system.
Book Tiles
Data Mining: Practical Machine Learning Tools and Techniques ...
catalogue Statistics, Data Analysis, and Decision Modeling
Software Architecture in Practice
Introduction to Algorithms
Software Engineering: A Practitioner’s Approach
Artificial Intelligence: A Modern Approach
Artificial Intelligence (Handbook Of Perception And Cognition)
result
Machine Learning (Mcgraw-Hill International Edit)
Prolog Programming for Artificial Intelligence
An Introduction to Support Vector Machines and Other Kernel-based
Learning Methods
Table 2 shows the results of our approach on the testing data provided by the
lab organizers. The difference between the two runs submitted to the lab only
lie in the different weights applied for containing the tags within the examples,
group name, tiles, text or the users book catalogue.
Fig. 2. Overview of the processing pipeline. At first the forum entry is parsed. In the
next step the books of the examples and catalogue are retrieved from the Amazon
dataset. The free text is analyzed for tags and browse nodes co-occurrences. For each
tags or browse nodes a corresponding weight is assigned representing the count within
all books and within the free text. Afterwards two queries are send to the index; one
containing the tags as keywords applied on the tag field within the index one with the
same procedure for the browse nodes. The resulting two lists are combined within the
utility matrix that combines and re-ranks both lists by the use of SVD.
Table 2. Official results of our submissions on the testing set provided by the lab
organizers. Reported measures are the normalized discounted cumulative gain at 10,
mean reciprocal rank, mean average precision, and recall at 1000.
run nDCG@10 MRR MAP R@1000
submission 2 0.0058 0.0227 0.0010 0.0013
submission 1 0.0018 0.0084 0.0004 0.0004
4 Discussion
Although our approach seemed to work fine in our initial pre-test the system
did not perform well in the official results provided by the organizer. Although
the system performed below our expectation we did not expect excellent results
since we did not optimize at all on the testing set.
5 Conclusion and Future Work
The presented content-based system is considered a first initial step towards a
competitive system. The next logical steps will be the optimization towards the
training dataset. As first approach to improve the results we consider to evaluate
different setting upon the weights. We also consider to use general learning to
rank approaches.
Acknowledgments
The presented work was developed within the EEXCESS project funded by the
European Union Seventh Framework Programme FP7/2007-2013 under grant
agreement number 600601. The Know-Center is funded within the Austrian
COMET Program - Competence Centers for Excellent Technologies - under the
auspices of the Austrian Federal Ministry of Transport, Innovation and Tech-
nology, the Austrian Federal Ministry of Economy, Family and Youth and by
the State of Styria. COMET is managed by the Austrian Research Promotion
Agency FFG.
References
1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender sys-
tems: A survey of the state-of-the-art and possible extensions. Knowledge and Data
Engineering, IEEE Transactions on 17(6), 734–749 (2005)
2. Beckers, T., Fuhr, N., Pharo, N., Nordlie, R., Fachry, K.N.: Overview and results of
the inex 2009 interactive track. In: Research and Advanced Technology for Digital
Libraries, pp. 409–412. Springer (2010)
3. Koolen, M., Bogers, T., Gäde, M., Hall, M., Huurdeman, H., Kamps, J., Skov,
M., Toms, E., Walsh, D.: Overview of the clef 2015 social book search lab. In:
Experimental IR Meets Multilinguality, Multimodality, and Interaction, pp. 545–
564. Springer (2015)
4. Miller, B.N., Konstan, J.A., Riedl, J.: Pocketlens: Toward a personal recommender
system. ACM Transactions on Information Systems (TOIS) 22(3), 437–476 (2004)
5. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decompo-
sition algorithms for highly scalable recommender systems. In: Fifth International
Conference on Computer and Information Science. pp. 27–28. Citeseer (2002)