Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

User evaluation outside the lab: the trial of Físchlár-News

A user study of Físchlár-News system was conducted in Spring 2004 with 16 users, each user using the system for a 1-month period. Físchlár-News is an experimental online news archive that incorporates various automatic content-based video indexing techniques and a news story recommender algorithm to process and index the daily 9 o’clock broadcast news from TV and allows its users to browse, search, be recommended, and play news stories on a conventional web browser. Pre and post-trial questionnaires, interaction logging and incident diary methods collected both qualitative and quantitative usage data during the trial period. While the details of the findings from this evaluation is reported elsewhere, in this paper we report the details of the methodology taken and our experience of conducting this evaluation

Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Goal-based structuring in a recommender systems

Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using recommendations) taking into account the users' immediate interests: a goal-based structuring method. Goal-based structuring is based on the fact that people experience certain gratifications from using information, which should match with their goals. An experiment using an electronic TV guide shows that structuring information using a goal-based structure makes it easier for users to find interesting information, especially if the goals are used explicitly; this is independent of whether recommendations are used or not. It also shows that goal-based structuring has more influence on how easy it is for users to find interesting information than recommendations

Multi-List Recommendations for Personalizing Streaming Content

The decision behind choosing a recommender system that yields accurate recommendations yet allows users to explore more content has been a topic of research in the last decades. This work attempts to find a recommender system for TV 2 Play, a movie streaming platform, that would perform well on implicit feedback data and provide multi-lists as recommenda- tions. Several approaches are examined for suitability, and Collaborative Filtering and Multi- Armed Bandits are decided upon. The models for each approach are built using the pipeline utilized by TV 2 Play. The models are then compared in performance on several evaluation metrics in the first stage of offline testing, yielding Alternating Least Squares and Bayesian Personalized Ranking as the best-performing models. The second stage of offline testing includes testing the two models and their variants with the BM25 weighting scheme applied against each other. The unweighted Bayesian Personalized Ranking model has shown the highest user-centric metrics while maintaining relatively high recommendation-centric met- rics, which led to that model being tested in online settings against the algorithm currently used by TV 2 Play team. The online testing has revealed that our model underperforms compared to the TV 2 Play model when used on the kids’ page but produces equally good results on the movies page. The results can be attributed to the differences in behavioral content consumption patterns between users.Master's Thesis in InformaticsINF399MAMN-PROGMAMN-IN