An exploration of improving collaborative recommender systems via user-item subgroups

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

Conformative Filtering for Implicit Feedback Data

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines

Scalable and interpretable product recommendations via overlapping co-clustering

We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201

DRec:Multidomain Recommendation System for Social Community

The Recommendation System is the software engines and the approaches for consideration proposal to the user which might be most probably matched with the liking of users. Usually, Recommendations system recommends on various fields like what items to buy, which movies to watch even the job recommendations, depending upon the users profile. Instinctively if the domains of users are captured and filtered out accordingly to recommend them will be a very useful idea. In this paper we will be discussing about the research done by us and the limitation of the system.We design a system for recommending domains in social network, using an explicit / offline data. We have tested it on two popular dataset namely Epinions and Ciao. The ratings of items are studied and performance measures are calculated with three different ways 1) MAP (Mean Average Precision) 2)F-measure and 3) nDCG (Normalized Discounted Cumulative Gain) . As well we have compared the results with 5 comparisons methods.All the techniques and methods are explained in paper. DOI: 10.17762/ijritcc2321-8169.15081

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective

Probabilistic Ensemble of Collaborative Filters

Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based framework to enhance the capability of a recommender in handling diverse data. Specifically, we formulate a probabilistic model which integrates the items, the users, as well as the associations between them into a generative process. On top of this formulation, we further derive a progressive algorithm to construct an ensemble of collaborative filters. In each iteration, a new filter is derived from re-weighted entries and incorporated into the ensemble. It is noteworthy that while the algorithmic procedure of our algorithm is apparently similar to boosting, it is derived from an essentially different formulation and thus differs in several key technical aspects. We tested the proposed method on three large datasets, and observed substantial improvement over the state of the art, including L2Boost, an effective method based on boosting.Comment: 8 pages. In Proceedings of AAAI-201