Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model

Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Current popular recommendation algorithms, typically based on matrix factorizations, often have high accuracy and achieve good clickthrough rates. However, diversity of the recommended items, which can greatly enhance user experiences, is often overlooked. Moreover, most algorithms do not produce interpretable uncertainty quantifications of the recommendations. In this work, we propose the Bayesian Mallows for Clicking Data (BMCD) method, which augments clicking data into compatible full ranking vectors by enforcing all the clicked items to be top-ranked. User preferences are learned using a Mallows ranking model. Bayesian inference leads to interpretable uncertainties of each individual recommendation, and we also propose a method to make personalized recommendations based on such uncertainties. With a simulation study and a real life data example, we demonstrate that compared to state-of-the-art matrix factorization, BMCD makes personalized recommendations with similar accuracy, while achieving much higher level of diversity, and producing interpretable and actionable uncertainty estimation.Comment: 27 page

Performance of re-ranking techniques used for recommendation method to the user CF- Model

The recent research work for addressed to the aims at a spectrum of item ranking techniques that would generate recommendations with far more aggregate variability across all users while retaining comparable levels of recommendation accuracy. Individual users and companies are increasingly relying on recommender systems to provide information on individual suggestions. The recommended technologies are becoming increasingly efficient because they are focusing on scalable sorting-based heuristics that make decisions based solely on "local" data (i.e., only on the candidate items of each user) rather than having to keep track of "national" data, such as items have been all user recommended at the time. The real-world rating datasets and various assessments to be the prediction techniques and comprehensive empirical research consistently demonstrate the proposed techniques' diversity gains. Although the suggested approaches have primarily concentrated on improving recommendation accuracy, other critical aspects of recommendation quality, such as recommendation delivery, have often been ignored

Delivery of Personalized and Adaptive Content to Mobile Devices:A Framework and Enabling Technology

Many innovative wireless applications that aim to provide mobile information access are emerging. Since people have different information needs and preferences, one of the challenges for mobile information systems is to take advantage of the convenience of handheld devices and provide personalized information to the right person in a preferred format. However, the unique features of wireless networks and mobile devices pose challenges to personalized mobile content delivery. This paper proposes a generic framework for delivering personalized and adaptive content to mobile users. It introduces a variety of enabling technologies and highlights important issues in this area. The framework can be applied to many applications such as mobile commerce and context-aware mobile services

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance

With Enough Eyeballs All Searches Are Diligent: Mobilizing the Crowd in Copyright Clearance for Mass Digitization

Digitization of 20th Century cultural heritage is severely restricted due to the real or potential subsistence of copyright and related rights. Under the laws on orphan works introduced in many countries, items whose copyright status is uncertain may possibly be lawfully digitized, on condition that a “diligent search” of the copyright owners has been performed. However, carrying out diligent searches on large collections is a lengthy and expensive process, which may discourage institutional users from embarking on large-scale digitization. While the problem of performing diligent searches has been so far approached in a “centralized” manner by individual institutions, the article suggests a de-centralized approach based on crowdsourcing certain phases of the diligent search process. The proposed solution may alleviate the problem of the high costs of diligent search, and may ultimately enable cultural heritage institutions to take full advantage of the orphan works legislation. Suitability of the crowdsourcing solution to the cultural heritage sector is discussed and challenges to implementation are identified

Maximizing Aggregate Recommendation Diversity: A GraphTheoretic Approach

Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes a graph-theoretic approach for maximizing aggregate recommendation diversity based on maximum flow or maximum bipartite matching computations. The proposed approach is evaluated using real-world movie rating datasets and demonstrates substantial improvements in both diversity and accuracy, as compared to the recommendation re-ranking approaches, which have been introduced in prior literature for the purpose of diversity improvement

Recommender systems in model-driven engineering: A systematic mapping review

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of researchThis work has been funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 813884 (Lowcomote [134]), by the Spanish Ministry of Science (projects MASSIVE, RTI2018-095255-B-I00, and FIT, PID2019-108965GB-I00) and by the R&D programme of Madrid (Project FORTE, P2018/TCS-431