Matrix Factorization Techniques for Recommender Systems

Please provide a short abstract of 100 to 250 words. The abstract should not contain any undefined abbreviations or unspecified references. Work planned but not completed should not appear in the abstract.

User Modeling and User-Adapted Interaction

People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such systems behave very differently from a human when asked for a recommendation. The goal of this paper is to begin to reduce this gap. In particular, humans can quickly establish preferences when asked to make a recommendation for someone they do not know. We address this cold-start recommendation problem in an online learning setting. We develop a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences. Taking advantage of latent structure in the recommendation space using a probabilistic latent factor model, our experiments with both synthetic and real world data compare different types of feedback and question selection strategies. We find that our framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions. Our results demonstrate dramatic benefits of starting from offline embeddings, and highlight the benefit of bandit-based explore-exploit strategies in this setting.

/pdf/towards-conversational-recommender-systems-t6x4xs04bw.pdf

Towards Conversational Recommender Systems

A survey of active learning in collaborative filtering recommender systems

In embodiments of the present invention improved capabilities are described for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of (1) ascertaining preferences of a plurality of users who are part of an internet based social interactive construct, wherein the plurality of users become a plurality of known users; (2) determining the internet based social interactive graphical representation for the plurality of known users; and (3) inferring the preferences of an unknown user present in the internet based social interactive graphical representation of the plurality of known users based on the interrelationships between the unknown user and the plurality of known users within the graphical representation.

Inferring user preferences from an internet based social interactive construct

Recommender systems learn user preferences and provide them personalized recommendations. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new-user problem. A simple and effective way to overcome this problem is posing queries to new users so that they express their preferences about selected items, e.g., by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e., ratings from users that will help most in determining their interests. Active learning is a learning algorithm that is able to interactively query the Oracle to obtain labels for data instances. The Oracle is a user or teacher who knows the labels. The aim of this dissertation [8] is to take inspiration from the literature of active learning for classification (regression) problems and develop new methods for the new-user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide ratings (labels) to items (data instances). Specifically, the following questions are addressed in this dissertation: (1) which recommendation model is suitable for active-learning purposes? (Sect. 2) (2) how can active learning criteria be adapted and customized for the new-user problem and which one is the best? (Sect. 3) (3) what are the specific requirements and properties of the new-user problem that do not exist in active learning and how can new active learning methods be developed based on these properties? (Sects. 4, 5).

https://www.ismll.uni-hildesheim.de/pub/pdfs/karimi-ki.pdf

Active Learning for Recommender Systems

Recommender systems help Web users to address information overload. However, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. In this paper, we consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user. The proposed method is based on Matrix Factorization (MF) which is a strong prediction model for recommender systems. First, the proposed method explores the latent space to get closer to the optimal new user parameters. Then, it exploits the learned parameters and slightly adjusts them. The results show that beside improving the accuracy of recommendation, MF approach also results in drastically reduced user waiting times, i.e., the time that the users wait before being asked a new query. Therefore, it is an ideal choice for using active learning in real-world applications of recommender systems.

/pdf/non-myopic-active-learning-for-recommender-systems-based-on-55opwnbpax.pdf

Non-myopic active learning for recommender systems based on Matrix Factorization

Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. However, different from the classic active learning, users (the "oracle") are not always able to provide an answer for queries. The easiest way to solve this problem is to ask most popular items, i.e items which have received many ratings from training users. But it is static and presents the same items to all users regardless of the ratings they have provided so far. In this paper we propose a method that improves the most popular selection strategy using the characteristics of matrix factorization. It finds similar users to the new user in the latent space and then selects item which is most popular among the similar users. The experimental results show the proposed method outperforms the most popular method both in terms of error and the number of received ratings.

/pdf/exploiting-the-characteristics-of-matrix-factorization-for-2ma5afwyjd.pdf

Exploiting the characteristics of matrix factorization for active learning in recommender systems

Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal active learning selects a query that directly optimizes the expected error for the test data. This approach is applicable for prediction models in which this question can be answered in closed-form given the distribution of test data is known. Unfortunately, there are many tasks and models for which the optimal selection cannot efficiently be found in closed-form. Therefore, most of the active learning methods optimize different, non-optimal criteria, such as uncertainty. Nevertheless, in this paper we exploit the characteristics of matrix factorization, which leads to a closed-form solution and by being inspired from existing optimal active learning for the regression task, develop a method that approximates the optimal solution for recommender systems. Our results demonstrate that the proposed method improves the prediction accuracy of MF.

/pdf/towards-optimal-active-learning-for-matrix-factorization-in-6of4skwt3g.pdf

Towards Optimal Active Learning for Matrix Factorization in Recommender Systems

Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.

/pdf/active-learning-for-aspect-model-in-recommender-systems-2uk8bowh0y.pdf

Rasoul Karimi

Papers

Active Learning for Recommender Systems

Non-myopic active learning for recommender systems based on Matrix Factorization

Exploiting the characteristics of matrix factorization for active learning in recommender systems

Towards Optimal Active Learning for Matrix Factorization in Recommender Systems

Active learning for aspect model in recommender systems