The most popular collaborative filtering implementations require either a critical mass of referenced resources or a lot of active users. Other solutions are based on finding a referral with an expertise on the given domain of discourse. In this article we present the social semantic collaborative filtering solution to information retrieval. We describe how the concept of users' managed collect...

Item-based collaborative filtering is becoming the most promising approach in recommender systems. It can predict an active user’s interest for a target item based on his observed ratings. With the user’s interests changing during interacting with collaborative filtering, the issue of concept drift is becoming a main factor impacting the accuracy of recommendation. Aiming at the issue of concep...

Collaborative filtering is one of the most widely researched and implemented recommendation algorithms. Collaborative filtering is simply a mechanism to filter massive amounts of data based upon a previous interactions of a large number of users. In this project I analyze and benchmark several collaborative filtering implementations in PowerGraph, an advanced machine learning framework, across ...

Collaborative filtering is an important personalized method in recommender systems in E-commerce. It is infeasible that traditional collaborative filtering is based on absolute rating for items since users are difficult to accurately make an absolute rating for items, and also different users give different rating distribution. In this paper, an improved collaborative filtering algorithm based ...

The most popular collaborative filtering implementations require either a critical mass of referenced resources and a lot of active users. Other solutions are based on finding a referral with an expertise on the given domain of discourse. In this article we present the semantic social collaborative filtering solution to information retrieval. We describe how the concept of users’ managed collec...

Recommender systems are web based systems that aim at predicting a customer's interest on available products and services by relying on previously rated products and dealing with the problem of information and product overload. Collaborative filtering is the most popular recommendation technique nowadays and it mainly employs the user item rating data set. Traditional collaborative filtering ap...

This paper considers the problem of matrix completion, when some number of the columns are arbitrarily corrupted, potentially by a malicious adversary. It is well-known that standard algorithms for matrix completion can return arbitrarily poor results, if even a single column is corrupted. What can be done if a large number, or even a constant fraction of columns are corrupted? In this paper, w...

With the increasing popularity of recommender systems in commercial services, the quality of recommendations has increasingly become an important to study, much like the quality of search results from search engines. While some users faithfully express their true opinion, many provide noisy or incorrect ratings which can be detrimental to the quality of the generated recommendations. The presen...

Due to dynamic nature of current software development methods, changes in requirements are embraced and given proper consideration. However, this triggers the rank reversal problem which involves re-prioritizing requirements based on stakeholders’ feedback. It incurs significant cost because of time elapsed in large number of human interactions. To solve this issue, a Semi-Automated Framework f...

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...