E-commerce, buying and selling of products by electronic means, has become popular due to the emergence of World Wide Web. One of the vital components of e-commerce systems is recommender systems (RSs). The RS is employed as a part of e-commerce system to help users in finding products of their interest from a huge number of available products. The Collaborative filtering (CF) approach is one o...

Rich geo-textual data is available online and the data keeps increasing at a high speed. We propose two user behavior models to learn several types of user preferences from geo-textual data, and a prototype system on top of the user preference models for mining and search geo-textual data (called PreMiner) to support personalized maps. Different from existing recommender systems and data analys...

Top-N evaluation of recommender systems, typically carried out using metrics from information retrieval or machine learning, has several challenges. Two of these challenges are popularity bias, where the evaluation intrinsically favors algorithms that recommend popular items, and misclassified decoys, where items for which no user relevance is known are actually relevant to the user, but the ev...

Various libraries have been released to support the development of recommender systems for some time, but it is only relatively recently that larger scale, open-source platforms have become readily available. In the context of such platforms, evaluation tools are important both to verify and validate baseline platform functionality, as well as to provide support for testing new techniques and a...

The TechLens+ strategy for addressing the recommender cold-start problem in a scholarly digital library is to seed the preference matrix with article references. However, this method generates boolean ratings rather than ratings on a numerical scale, as is more typical with recommender systems for commodity products. One strategy for generating a better preference matrix for collaborative filte...

Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top...