Mood-Aware Recommendation

Traditional recommender systems assume that a user's preferences don't change from one day to the next. In reality, users' interests may vary contextually, based on mood, social setting, or other factors. My current project explores how a user's preferences are influenced by their emotional state. I am designing a recommender algorithm that uses knowledge of a user's current emotional state to improve prediction accuracy. The algorithm uses social tags as a bridge between emotional state and item preferences. By understanding the relationship between tags and mood, I also hope to gain insight into mood-aware tag recommendation.

The Tag Genome

Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less "dark" movie. This project introduces Movie Tuner, a novel system that supports navigation from one item to nearby items along dimensions represented by tags.

Movie Tuner is based on an underlying data structure called the tag genome. The tag genome encodes each item's relationship to a common set of tags by performing machine learning on user-generated content. We compute the tag genome using a hierarchical regression model that predicts the relevance of an arbitrary (item, tag) pair using features extracted from tags, ratings, and text reviews. We train the model using a gold standard of 50,203 (item, tag) relevance values provided by users.

Tag Expression

Tag expression is a novel form of preference elicitation that combines elements from tagging and rating systems. Tag expression enables users to apply affect to tags to indicate whether the tag describes a reason they like, dislike, or are neutral about a particular item. In this project we design a user interface for applying affect to tags, as well as a technique for visualizing the overall community's affect. By analyzing 27,773 tag expressions from 553 users entered in a 3-month period, we empirically evaluate our design choices. We also conduct a survey of 97 users that explores users' motivations in tagging and measures user satisfaction with tag expression.

Tagsplanations

While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. In this project we introduce tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.

Tagommenders

Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual comprehensibility inherent in tagging systems. This project explores tagommenders, recommender algorithms that predict users' preferences for items based on their inferred preferences for tags. We develop tag preference inference algorithms based on users' interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users' ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to flexible recommender systems that leverage the characteristics of items users find most important.

Tag Quality Prediction

Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. This project explores tag selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.