Beyond Relevance

Abstract

The design and evaluation of tag recommendation methods has historically focused on maximizing the relevance of the suggested tags for a given object, such as a movie or a song. However, relevance by itself may not be enough to guarantee recommendation usefulness. Promoting novelty and diversity in tag recommendation not only increases the chances that the user will select “some” of the recommended tags but also promotes complementary information (i.e., tags), which helps to cover multiple aspects or topics related to the target object. Previous work has addressed the tag recommendation problem by exploiting at most two of the following aspects: (1) relevance, (2) explicit topic diversity, and (3) novelty. In contrast, here we tackle these three aspects conjointly, by introducing two new tag recommendation methods that cover all three aspects of the problem at different levels. Our first method, called Random Forest with topic-related attributes , or RF t , extends a relevance-driven tag recommender based on the Random Forest ( RF ) learning-to-rank method by including new tag attributes to capture the extent to which a candidate tag is related to the topics of the target object. This solution captures topic diversity as well as novelty at the attribute level while aiming at maximizing relevance in its objective function. Our second method, called Explicit Tag Recommendation Diversifier with Novelty Promotion , or xTReND , reranks the recommendations provided by any tag recommender to jointly promote relevance, novelty, and topic diversity. We use RF t as a basic recommender applied before the reranking, thus building a solution that addresses the problem at both attribute and objective levels. Furthermore, to enable the use of our solutions on applications in which category information is unavailable, we investigate the suitability of using latent Dirichlet allocation (LDA) to automatically generate topics for objects. We evaluate all tag recommendation approaches using real data from five popular Web 2.0 applications. Our results show that RF t greatly outperforms the relevance-driven RF baseline in diversity while producing gains in relevance as well. We also find that our new xTReND reranker obtains considerable gains in both novelty and relevance when compared to that same baseline while keeping the same relevance levels. Furthermore, compared to our previous reranker method, xTReD , which does not consider novelty, xTReND is also quite effective, improving the novelty of the recommended tags while keeping similar relevance and diversity levels in most datasets and scenarios. Comparing our two new proposals, we find that xTReND considerably outperforms RF t in terms of novelty and diversity with only small losses (under 4%) in relevance. Overall, considering the trade-off among relevance, novelty, and diversity, our results demonstrate the superiority of xTReND over the baselines and the proposed alternative, RF t . Finally, the use of automatically generated latent topics as an alternative to manually labeled categories also provides significant improvements, which greatly enhances the applicability of our solutions to applications where the latter is not available.