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

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

A Collaborative Filtering Recommender System For Infrequently Purchased Product Using Slope-One Algorithm And Association Rule Mining

Nowadays, tourism industry are actively being utilised in generating a state or country income. In order to attract tourist from all over places, information conveyance is important. Traditionally, people travels to certain places based on oral recommendation by families and friends. Now, people tends to go travel based on reviews that are read from blogs and websites. But, this leads to overflow of unfiltered information. In order to effectively recommending places to travel for tourist, recommendation engine are being developed. Most recommendation engine has suffice information to make recommendation for example Amazon.com recommendation and Google.com recommendation. Meanwhile, in tourism it is quite challenging in making recommendation because hotels are occasionally being booked or purchased by consumer. This is due to the fact that travelling are expensive and time consuming. This project implement the collaborative filtering using slope-one algorithm and also implement association rule mining in recommending hotels for tourist. This recommender system uses slope-one algorithm whereby it accumulate and takes into account of the difference in popularity. The objective of this project to study different types of recommendation techniques for infrequently purchased products and to investigate technique and dataset that are suitable to implement in recommending infrequently purchased products. As a conclusion, this collaborative filtering recommendation system will help user in decision making. Further research on other approaches in implementing recommender system in tourism domain can help in information delivery

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD