Session: Social Networks & Recommenders

Date: Friday, October 24, 11:00-12:30

• Tag recommendations based on tensor dimensionality reduction

  by Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos

  Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.

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• Social ranking: uncovering relevant content using tag-based recommender systems

  by Valentina Zanardi, Licia Capra

  Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0. We measure users’ similarity based on their past tag activity. We infer tags’ relationships based on their association to content. We then propose a mechanism to answer a user’s query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. A thorough evaluation conducted on the CiteULike dataset demonstrates that Social Ranking neatly improves coverage, while not compromising on accuracy.

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• Recommending topics for self-descriptions in online user profiles

  by Werner Geyer, Casey Dugan, David R. Millen, Michael Muller, Jill Freyne

  Traditional social networking sites allow users to enter responses to a set of predefined fields when populating their personal profiles. In the system discussed in this work, freeform ‘About You’ entries allow users to craft their own questions / topics. We found that this kind of flexibility often leads to low content contributions and infrequent updates. The ‘About You’ recommender system described in this paper differs from many recommender systems in that it recommends content for users to create, rather than consume. We present empirical data from an experiment with 2,000 users of a social networking site during a one month period. Our findings suggest that users who receive recommendations create more entries and update them more over time. Further, using articulated social network information for recommendations performed better than content-based matching.

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• Personalized, interactive tag recommendation for flickr

  by Nikhil Garg, Ingmar Weber

  We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust.

  Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a user’s tagging history) can or cannot be improved by global schemes (using everybody’s tagging history).

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