Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec, and we summarize and reflect on four use cases that have been implemented with TagRec: (i) tag recommendations, (ii) resource recommendations, (iii) recommendation evaluation, and (iv) hashtag recommendations. To date, TagRec served the development and or evaluation process of tag-based recommender systems in two large scale European research projects, which have been described in 17 research papers. Thus, we believe that this work is of interest for both researchers and practitioners of tag-based recommender systems.
A considerable contribution to this area is http: wiki.librec.net doku.php , a Java-based library that, so far, comprises around 70 resource recommendation algorithms and evaluation modules @cite_6 . Another Java-based, open-source framework is http: ranksys.org , which focuses on the evaluation of ranking problems and supports the investigation of novelty as well as diversity for academic research @cite_29 , which is reflected in its design (e.g., data input interfaces work with a triple of user, item and features).
@cite_29 @cite_6
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The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance. The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation. In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice. By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation. This paper presents HYRIWYG (How You Rate Influences What You Get), a reputation system applicable to Internet Recommendation Systems (RS). The novelty lies in the incentive mechanism that encourages evaluators to volunteer their true opinion. Honesty is encouraged because rewards are indexed by the quality of the RS's suggestions. One-Class Collaborative Filtering (OCCF) has recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, the problem with previous research studies on OCCF is that they focused on either rating prediction or ranking prediction, but no concerted research effort has been devoted to developing a recommendation approach that simultaneously optimizes both the ratings and rank of the recommended items. In order to overcome the defects of prior research, a new better unified OCCF approach (UOCCF) based on the newest Collaborative Less-is-More Filtering (CLiMF) approach and the Probabilistic Matrix Factorization (PMF) approach was proposed, which benefits from the ranking-oriented perspective and the rating-oriented perspective by sharing common latent features of users and items in CLiMF and PMF. We also provide an efficient learning algorithm to solve the optimization problem for UOCCF. Experimental results on practical datasets showed that our proposed UOCCF approach outperformed existing OCCF approaches (both ranking-oriented and rating-oriented) over different evaluation metrics, and that the UOCCF approach enjoys the advantage of low complexity and is shown to be linear with the number of observed ratings in a given useritem rating matrix. Because of its high precision and good expansibility, UOCCF is suitable for processing big data, and has wide application prospects in the field of internet information recommendation.