Multi Feature Space LDA for Tag Recommendation in Cold Start Problem
Published:
Multi Feature Space LDA for Tag Recommendation in Cold Start Problem by Muhammad Ali Masood (2013)
Social tagging is a popular way of organizing, sharing and browsing information between groups and individuals. Users of social bookmarking sites annotate resources with keywords called tags. These tags become part of resource and user profiles. Tags are useful in many applications like information seeking, information representation, and search etc. Tags are also helpful in developing user friendly interfaces like tag clouds and faceted search. Even though information is increasing day by day, still in the case of sharing new resources (the cold start problem), problems occur during the process of tagging because the sharing platform does not have any existing information about the newly added resource. In this thesis we propose a novel model MFS-LDA in tag recommendation for the cold start problem. MFS-LDA removes biassness by separating the contents, the titles in the model. In addition, due to separation of feature spaces MFS-LDA can disambiguate the context of the resource. In addition, MFS-LDA gained previous heuristics information, users/community perspective by modeling tags feature space. Moreover, MFS-LDA introduces dependency among topics of different feature spaces. Dependency helps in recommending subjective tags by considering opinion from each feature space in topic assignment. In addition, dependency also helps in disambiguation process. Moreover, dependency in MFS-LDA helps in recommending blend of context by recommending tags from each feature space. We evaluate MFS-LDA on various baselines. With the use of dependency MFSLDA recommends more subjective tags instead of generic tags. We evaluate our model on a large dataset consisting of 21,000 unique resources. MFS-LDA shows a significant improvement over different baselines. In the end, MFS-LDA outperforms different baselines even if any entity fails to provide useful information.