The emerging of social community generates huge amount useful information in various areas. The information is generated in the context of social relation between people and their friends and is helpful to applications in the context. In the social book search task, we integrate the social feature into the traditional information retrieval technology to give better recommendation on books. We submitted four runs in the INEX 2013 Social book search track, the paper reports the results and discussions.
The emerging of social community generates huge amount useful information in various areas. The information is generated in the context of social relation between people and their friends and is helpful to applications in the context. In the book search application, the result of traditional information retrieval technology is not enough for the users who need more personal recommendation. Recommendation from friends are more appealing, it might contain more personal feeling and cover more subtle reasons that traditional information retrieval system cannot cover. To combine the two information source of book recommendation, we integrate the social feature into the traditional information retrieval technology to give better recommendation on books. In this task, user-generated metadata is used as the social feature.
The structure of this paper is as follows. Section 2 is the data set description, section 3 shows our architecture and some details, section 4 is the experiment results, and final section gives conclusions.
The document collection in this task is provided by the INEX 2013
social book search track. The documents are in XML format, about 2.8
million books, and the size is 24GB. These documents were collected
from Amazon.com and LibraryThing. Table 1 lists all the XML tag
used in Social Book Search Track[
Topic set is also provided by INEX 2013 Social Book Search track, which is collected from LibraryThing. A topic describes the information need of a user. Figure 1 gives an example, the XML tags used are : <topic id>,<query>,<title>,<group>,<member>, and <narrative>.
The index and search engine in used is the Lucene system, which is an
open source full text search engine provided by Apache software
foundation. Lucene is written in JAVA and can be called by JAVA program
easily to build various applications [
According to (Bogers and Larsen, 2012) [
In order to make string matching easier, the content in the <dewey> tag will be restored to strings accordint to the 2003 list of Dewey category descriptions, for example: <dewey>004</dewey> will be restored to <dewey>Data processing Computer science</dewey>. Also, the content of <tag> will be expanded, for example: <tag count="3">fantasy</tag> will be expanded as <tag>fantasy fantasy fantasy</tag>.
The 19 tags were used to build our index file. In additional to the 19 tags, we also index the content of <review> as an independent index We integrate the user-generated metadata into the traditional contentbased search result by re-ranking the results.
The social features are used to give more weight on certain books, for example User rating: users might vote a book from 1 to 5, the higher the better. Helpful vote: other users might endorse one comment by voting it as helpful. Total vote: the total number of helpful or not.
We designed 3 different ways to use these social features in reranking. 1) User Rating method
Increase the weight of content-based retrieval result by adding the summation of user rating. As shown in formula (1):
Scorere−ranked(i) = α ∗ Scoreorg(i) + (1 − α) ∗ Scoreuser rating(i)
In our experiment, the content of <query> tag is used as the query. The α is set as 0.9. We sent 6 runs; the results are shown in Table 2. The setting of each run is as follows.
Run1.query.content-base Search the index file build from 19 tags with content-based search.
This paper reports our system and result in INEX 2013 Social Book Search track. We sent 6 runs and the results are list in Table 2. In the six runs, run4 give best nDCG@10. Run4 is searching with content-based search and re-ranking with weights user rating, which shows that helpful review is more useful than average user rating. In the future, we will expand the query with the content in more tags. In our experiment, α=0.9 is a tentative trial, more experiment will be necessary to get the best parameter. 6
This study is conducted under the "Digital Convergence Service Open Platform" of the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the Republic of China.