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216
Recommender Systems
"... The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of ..."
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The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. For example, movie watchers on Netflix frequently provide ratings on a scale of 1 (disliked) to 5 (liked). Such a data source records the quality of interactions between users and items. Additionally, the system may have access to user-specific and item-specific profile attributes such as demographics and product descriptions respectively. Recommender systems differ in the way they analyze these data sources to develop notions of affinity between users and items which can be used to identify well-matched pairs. Collaborative Filtering systems analyze historical interactions alone, while Content-based Filtering systems are based on profile attributes; and Hybrid techniques attempt to combine both of these designs. The architecture of recommender systems and their evaluation on real-world problems is an active area of research. 2
Functional Matrix Factorizations for Cold-Start Recommendation
"... A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitatio ..."
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A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy. In this paper, we present functional matrix factorization (fMF), a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles. Specifically, fMF constructs a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to her prior responses. More importantly, we associate latent profiles for each node of the tree — in effect restricting the latent profiles to be a function of possible answers to the interview questions — which allows the profiles to be gradually refined through the interview process based on user responses. We develop an iterative optimization algorithm that alternates between decision tree construction and latent profiles extraction as well as a regularization scheme that takes into account of the tree structure. Experimental results on three benchmark recommendation data sets demonstrate that the proposed fMF algorithm significantly outperforms existing methods for cold-start recommendation.
Bayesian bias mitigation for crowdsourcing
- In Neural Information Processing Systems
, 2011
"... Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling their responses is still being developed. A typical crowd-sourcing application can be divided into three steps: data collection, data cura-tion, and learning. At present these steps are often treated se ..."
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Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling their responses is still being developed. A typical crowd-sourcing application can be divided into three steps: data collection, data cura-tion, and learning. At present these steps are often treated separately. We present Bayesian Bias Mitigation for Crowdsourcing (BBMC), a Bayesian model to unify all three. Most data curation methods account for the effects of labeler bias by modeling all labels as coming from a single latent truth. Our model captures the sources of bias by describing labelers as influenced by shared random effects. This approach can account for more complex bias patterns that arise in ambigu-ous or hard labeling tasks and allows us to merge data curation and learning into a single computation. Active learning integrates data collection with learning, but is commonly considered infeasible with Gibbs sampling inference. We propose a general approximation strategy for Markov chains to efficiently quantify the effect of a perturbation on the stationary distribution and specialize this approach to ac-tive learning. Experiments show BBMC to outperform many common heuristics. 1
Collaborative Filtering for People to People Recommendation
- in Social Networks,” in AI 2010: Advances in Artifical Intelligence
, 2010
"... Abstract. Predicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have ..."
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Abstract. Predicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have a dual role as both “users ” and “items”, e.g., both initiating and receiving contacts. Here the assumption of active users and passive items in traditional collaborative filtering is inapplicable. In this paper we propose a model that fully captures the bilateral role of user interactions within a social network and formulate collaborative filtering methods to enable people to people recommendation. In this model users can be similar to other users in two ways – either having similar “taste ” for the users they contact, or having similar “attractiveness ” for the users who contact them. We develop SocialCollab, a novel neighbourbased collaborative filtering algorithm to predict, for a given user, other users they may like to contact, based on user similarity in terms of both attractiveness and taste. In social networks this goes beyond traditional, merely taste-based, collaborative filtering for item selection. Evaluation of the proposed recommender system on datasets from a commercial online social network show improvements over traditional collaborative filtering. Key words: MachineLearning,RecommenderSystems,CollaborativeFiltering 1
A Comparative Study of Collaborative Filtering Algorithms,” [Online] arXiv: 1205.3193
, 2012
"... Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collabo-rative filtering techniques – both classic and ..."
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Cited by 17 (4 self)
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Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collabo-rative filtering techniques – both classic and recent state-of-the-art – in a variety of experimental contexts. Specifically, we report conclusions controlling for number of items, number of users, sparsity level, performance criteria, and computational com-plexity. Our conclusions identify what algorithms work well and in what conditions, and contribute to both industrial deployment collaborative filtering algorithms and to the research community. 1
Collaborative topic regression with social matrix factorization for recommendation systems,” arXiv preprint arXiv:1206.4684
, 2012
"... Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popu-lar platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user’s social circle in their deci-sion process. In this paper, we are in ..."
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Cited by 16 (0 self)
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Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popu-lar platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user’s social circle in their deci-sion process. In this paper, we are interested in examining the effectiveness of social net-work information to predict the user’s rat-ings of items. We propose a novel hierar-chical Bayesian model which jointly incorpo-rates topic modeling and probabilistic matrix factorization of social networks. A major ad-vantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative fil-tering from the training data. Empirical ex-periments on two large-scale datasets show that our algorithm provides a more effective recommendation system than the state-of-the art approaches. Our results reveal interesting insight that the social circles have more influ-ence on people’s decisions about the useful-ness of information (e.g., bookmarking pref-erence on Delicious) than personal taste (e.g., music preference on Lastfm). We also ex-amine and discuss solutions on potential in-formation leak in many recommendation sys-tems that utilize social information.
User Feedback as a First Class Citizen in Information Integration Systems
, 2011
"... User feedback is gaining momentum as a means of addressing the difficulties underlying information integration tasks. It can be used to assist users in building information integration systems and to improve the quality of existing systems, e.g., in dataspaces. Existing proposals in the area are con ..."
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Cited by 16 (2 self)
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User feedback is gaining momentum as a means of addressing the difficulties underlying information integration tasks. It can be used to assist users in building information integration systems and to improve the quality of existing systems, e.g., in dataspaces. Existing proposals in the area are confined to specific integration sub-problems considering a specific kind of feedback sought, in most cases, from a single user. We argue in this paper that, in order to maximize the benefits that can be drawn from user feedback, it should be considered and managed as a first class citizen. Accordingly, we present generic operations that underpin the management of feedback within information integration systems, and that are applicable to feedback of different kinds, potentially supplied by multiple users with different expectations. We present preliminary solutions that can be adopted for realizing such operations, and sketch a research agenda for the information integration community.
A sequential recommendation approach for interactive personalized story generation
- Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems
, 2012
"... In story-based games or other interactive story systems, a Drama Manager is an omniscient agent that acts to bring about a particular sequence of plot points for the user to experience. We present a Drama Manager that uses player modeling to personalize the user’s story according to his or her story ..."
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Cited by 15 (7 self)
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In story-based games or other interactive story systems, a Drama Manager is an omniscient agent that acts to bring about a particular sequence of plot points for the user to experience. We present a Drama Manager that uses player modeling to personalize the user’s story according to his or her storytelling preferences. In order to deliver personalized stories, a Drama Manager must make decisions on not only which plot points to be included into the unfolding s-tory but also the optimal sequence of the events the user should experience. A prefix based collaborative filtering algorithm based on users ’ structural feedback is proposed to address the sequential selection problem. We demonstrate our system on a simple interactive story generation system based on choose-your-own-adventure stories to evaluate our algorithms. Results on human users and simulated users show that our Drama Manager is capable of capturing users’ preference and generating personalized stories with high accuracy.
Asking the Right Questions in Crowd Data Sourcing
"... Abstract—Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work we target the problem of gathering data from the crowd in an economical and principled fashion. We present AskIt! , a system that allows inte ..."
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Cited by 15 (6 self)
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Abstract—Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work we target the problem of gathering data from the crowd in an economical and principled fashion. We present AskIt! , a system that allows interactive data sourcing applications to effectively determine which questions should be directed to which users for reducing the uncertainty about the collected data. AskIt! uses a set of novel algorithms for minimizing the number of probing (questions) required from the different users. We demonstrate the challenge and our solution in the context of a multiple-choice question game played by the ICDE’12 attendees, targeted to gather information on the conference’s publications, authors and colleagues. I.
An exploration of improving collaborative recommender systems via user-item subgroups
- In Proc. of WWW
, 2012
"... Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users ..."
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Cited by 14 (1 self)
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Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have to-tally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item ma-trix. In this paper, to find meaningful subgroups, we for-mulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.
