USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS

On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But users’ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations

Finding new music: a diary study of everyday encounters with novel songs

This paper explores how we, as individuals, purposefully or serendipitously encounter 'new music' (that is, music that we haven’t heard before) and relates these behaviours to music information retrieval activities such as music searching and music discovery via use of recommender systems. 41 participants participated in a three-day diary study, in which they recorded all incidents that brought them into contact with new music. The diaries were analyzed using a Grounded Theory approach. The results of this analysis are discussed with respect to location, time, and whether the music encounter was actively sought or occurred passively. Based on these results, we outline design implications for music information retrieval software, and suggest an extension of 'laid back' searching

Learning Tree-based Deep Model for Recommender Systems

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201

Time-Bin-Based Neighbourhood Algorithm for Temporal Effects in Recommendation Systems

Recommender systems are used in various applications to boost the prediction accuracy of user preferences. The recent developments in recommendation frameworks support precise user decisions on any item depending on the actions of logged users. Although the existing algorithms exhibit good performance, some temporal aspects of user data require attention. This study introduces a new algorithm that utilises the users\u27 temporal effects by extracting time-bins as recent rating timelines. After error-function-based analyses for the optimal time-bins, the time-bin-based algorithm is employed to filter the best neighbours. Analyses show that the optimal time-bin size is 41 for the MovieLens dataset while 48 for the Netflix Prize dataset. Therefore, considering the cold start problem, a flexible time-bin approach is also proposed. The time-bin-based algorithm offers improvements of 7,44% (MovieLens) and 5,36% (Netflix) for the Matthews correlation coefficient and increases the balanced accuracy by 3,78% (MovieLens) and 2,06% (Netflix). Negative predictive value and specificity reveal high percentages for most rating classes, similar to the state-of-the-art approach. Finally, the standard accuracy metric demonstrates an improvement of 1,86% for MovieLens and 2,36% for the Netflix dataset

Automatic User Profile Construction for a Personalized News Recommender System Using Twitter

Modern society has now grown accustomed to reading online or digital news. However, the huge corpus of information available online poses a challenge to users when trying to find relevant articles. A hybrid system “Personalized News Recommender Using Twitter’ has been developed to recommend articles to a user based on the popularity of the articles and also the profile of the user. The hybrid system is a fusion of a collaborative recommender system developed using tweets from the “Twitter” public timeline and a content recommender system based the user’s past interests summarized in their conceptual user profile. In previous work, a user’s profile was built manually by asking the user to explicitly rate his/her interest in a category by entering a score for the corresponding category. This is not a reliable approach as the user may not be able to accurately specify their interest for a category with a number. In this work, an automatic profile builder was developed that uses an implicit approach to build the user’s profile. The specificity of the user profile was also increased to incorporate fifteen categories versus seven in the previous system. We concluded with an experiment to study the impact of automatic profile builder and the increased set of categories on the accuracy of the hybrid news recommender syste

From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the `best' products may not be the most `accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.Comment: 11 pages, 7 figure