A book recommendation system based on named entities

Recommendation systems are extensively used for suggesting new items to users and play an important role in the discovery of relevant new items, be it books, movies or music. An effective recommendation system should provide heterogeneous results and should not be biased towards only the most popular items. Books are particularly well-suited to content-based filtering as they are now widely available in digital formats which can allow various text mining approaches to dig out content related information. This paper presents a framework to develop a content-based recommendation system for books which can further be integrated with a collaborative filtering model. The proposed content-based recommender will use the Named Entities as the basic criteria to rank books and give recommendations

Graph-RAT: Combining data sources in music recommendation systems

The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experiment—indicative of the sort of procedure that can be configured using the toolkit—is provided to illustrate its usefulness

A Framework Design for Integrating Knowledge Graphs into Recommendation Systems

Online recommendation is a significant research domain in artificial intelligence. A recommendation system recommends different items to users, and has applications in varied domains, including news, music, movies, etc. Initially, recommendation systems were based on demographic, content-based filtering and collaborative filtering. But collaborative filtering often suffers from sparsity and cold start problems, therefore, side information is often used to address these issues and improve recommendation performance. Currently, incorporating knowledge into the recommendation algorithm has attracted increasing attention, as it can help improve recommendation system performance. Knowledge graph representation and construction, and recommendation system development are independent but related; the triples of knowledge graph form the input to the recommendation system. While, there are a number of independent solutions for each of these two tasks, currently, there is no existing solution that can combine the construction of knowledge graph and input it to the recommendation system to provide an integrated work pipeline. Our major contribution is a modular, easy to use framework solution that fills this gap, essentially enabling integration of a structured knowledge graph and a recommendation system. Our framework provides multiple functionalities, including cross-language invocation and pipeline execution mechanism, and also knowledge graph query, modification and visualization. We instantiate our implementation of the proposed framework and evaluate its performance to show that we achieve higher accuracy in recommendations by using side information extracted from knowledge graphs. Our framework addresses the complete pipeline from constructing structured data knowledge graph to training recommendation model to incorporating the recommendation system into application domains

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field