A study of the information needs of the users of a folk music library and the implications for the design of a digital library system

A qualitative study of user information needs is reported, based on a purposive sample of users and potential users of the Vaughan Williams Memorial Library, a small specialist folk music library in North London. The study set out to establish what the user’s (both existing and potential) information needs are, so that the library’s online service may take them into account with its design. The information needs framework proposed by Nicholas (2000) is used as an analytical tool to achieve this end. The demographics of the users were examined in order to establish four user groups: Performer, Academic, Professional and Enthusiast. Important information needs were found to be based on social interaction, and key resources of the library were its staff, the concentration of the collection and the library’s social nature. A collection of broad design requirements are proposed based on the analysis and this study also provided some insights into the issue of musical relevance, which are discussed

Highly accurate recommendation algorithm based on high-order similarities

In this Letter, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the standard Pearson coefficient, the user-user similarities are obtained by a diffusion process. Furthermore, by considering the second order similarities, we design an effective algorithm that depresses the influence of mainstream preferences. The corresponding algorithmic accuracy, measured by the ranking score, is further improved by 24.9% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the algorithm based on second order similarity can outperform the MCF simultaneously in all three criteria

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

HRS-CE: a hybrid framework to integrate content embeddings in recommender systems for cold start items

Recommender systems (RSs) provide the personalized recommendations to users for specific items in a wide range of applications such as e-commerce, media recommendations and social networking applications. Collaborative Filtering (CF) and Content Based (CB) Filtering are two methods which have been employed in implementing the recommender systems. CF suffers from Cold Start (CS) problem where no rating records (Complete Cold Start CSS) or very few records (Incomplete Cold Start ICS) are available for newly coming users and items. The performance of CB methods relies on good feature extraction methods so that the item descriptions can be used to measure items similarity as well as for user profiling. This paper addresses the CS problem by providing a novel way of integrating content embeddings in CF. The proposed algorithm (HRS-CE) generates the user profiles that depict the type of content in which a particular user is interested. The word embedding model (Word2Vec) is used to produce distributed representation of items descriptions. The higher representation for an item description, obtained using content embeddings, are combined with similarity techniques to perform rating predictions. The proposed method is evaluated on two public benchmark datasets (MovieLens 100k and MovieLens 20M). The results demonstrate that the proposed model outperforms the state of the art recommender system models for CS items

Rocchio Algorithm to Enhance Semantically Collaborative Filtering

International audienceRecommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommendation system combines the two techniques. In this paper, we present another hybridization approach: User Semantic Collaborative Filtering. The aim of our approach is to predict users preferences for items based on their inferred preferences for semantic information of items. In this aim, we design a new user semantic model to describe the user preferences by using Rocchio algorithm. Due to the high dimension of item content, we apply a latent semantic analysis to reduce the dimension of data. User semantic model is then used in a user-based collaborative filtering to compute prediction ratings and to provide recommendations. Applying our approach to real data set, the MoviesLens 1M data set, significant improvement can be noticed compared to usage only approach, content based only approach

Recommender systems: Interfaces and Architectures

"Our goal is to provide our 20 million users with 20 millio