Heat Conduction Process on Community Networks as a Recommendation Model

Using heat conduction mechanism on a social network we develop a systematic method to predict missing values as recommendations. This method can treat very large matrices that are typical of internet communities. In particular, with an innovative, exact formulation that accommodates arbitrary boundary condition, our method is easy to use in real applications. The performance is assessed by comparing with traditional recommendation methods using real data.Comment: 4 pages, 2 figure

Improving information filtering via network manipulation

Recommender system is a very promising way to address the problem of overabundant information for online users. Though the information filtering for the online commercial systems received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e. low recommendation accuracy for the small degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improve the recommendation performance. Specifically, it not only improve the recommendations accuracy (especially for the small degree items), but also help the recommender systems generate more diverse and novel recommendations.Comment: 6 pages, 5 figure

How to project a bipartite network?

The one-mode projecting is extensively used to compress the bipartite networks. Since the one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?Comment: 7 pages, 4 figure

The reinforcing influence of recommendations on global diversification

Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and collaborative filtering reinforce the popularity of hot items, widening the popularity dispersion. On the other hand, the heat conduction algorithm increases the popularity of the niche items and generates smaller dispersion of item popularity. Simulations are compared to mean-field predictions. Our results suggest that recommender systems have reinforcing influence on global diversification.Comment: 6 pages, 6 figure

Learning Users’ Interests in a Market-Based Recommender System

Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. Our marketplace thus coordinates multiple recommender agents and ensures only the best recommendations are presented. To do this effectively, however, each agent needs to learn the users’ interests and adapt its recommending behaviour accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommender agents can use for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

Research Paper Recommender System with Serendipity Using Tweets vs. Diversification

21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Kuala Lumpur, Malaysia, November 4–7, 2019. Part of the Lecture Notes in Computer Science book series (LNCS, volume 11853), also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 11853).So far, a lot of works have studied research paper recommender systems. However, most of them have focused only on the accuracy and ignored the serendipity, which is an important aspect for user satisfaction. The serendipity is concerned with the novelty of recommendations and to which extent recommendations positively surprise users. In this paper, we investigate a research paper recommender system focusing on serendipity. In particular, we examine (1) whether a user’s tweets lead to a generation of serendipitous recommendations and (2) whether the use of diversification on a recommendation list improves serendipity. We have conducted an online experiment with 22 subjects in the domain of computer science. The result of our experiment shows that tweets do not improve the serendipity, despite their heterogeneous nature. However, diversification delivers serendipitous research papers that cannot be generated by a traditional strategy

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users' interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.Comment: 20 pages book chapte

YourMOOC4all: a recommender system for MOOCs based on collaborative filtering implementing UDL

YourMOOC4all is a pilot research project to collect feedback requests regarding accessible design for Massive Open Online Courses (MOOCs). In this online application, a specific website offers the possibility for any learner to freely judge if a particular MOOC complies Universal Design for Learning (UDL) principles. User feedback is of great value for the future development of MOOC platforms and MOOC educational resources, as it will help to follow De-sign for All guidelines. YourMOOC4all is a recommender system which gathers valuable information directly from learners to improve aspects such as the quality, accessibility and usability of this online learning environment. The final objective of collecting user’s feedback is to advice MOOC providers about the missing means for meeting learner needs. This paper describes the pedagogical and technological background of YourMOOC4all and its use cases