Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space

Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality

Matrix factorization (MF) is extensively used to mine the user preference from explicit ratings in recommender systems. However, the reliability of explicit ratings is not always consistent, because many factors may affect the user's final evaluation on an item, including commercial advertising and a friend's recommendation. Therefore, mining the reliable ratings of user is critical to further improve the performance of the recommender system. In this work, we analyze the deviation degree of each rating in overall rating distribution of user and item, and propose the notion of user-based rating centrality and item-based rating centrality, respectively. Moreover, based on the rating centrality, we measure the reliability of each user rating and provide an optimized matrix factorization recommendation algorithm. Experimental results on two popular recommendation datasets reveal that our method gets better performance compared with other matrix factorization recommendation algorithms, especially on sparse datasets

BERT4Loc: BERT for Location -- POI Recommender System

Recommending points of interest is a difficult problem that requires precise location information to be extracted from a location-based social media platform. Another challenging and critical problem for such a location-aware recommendation system is modelling users' preferences based on their historical behaviors. We propose a location-aware recommender system based on Bidirectional Encoder Representations from Transformers for the purpose of providing users with location-based recommendations. The proposed model incorporates location data and user preferences. When compared to predicting the next item of interest (location) at each position in a sequence, our model can provide the user with more relevant results. Extensive experiments on a benchmark dataset demonstrate that our model consistently outperforms a variety of state-of-the-art sequential models

Analisis dan Implementasi Cluster-Smoothed pada Collaborative Filtering

Abstrak Collaborative filtering adalah salah satu pendekatan yang biasa digunakan pada sistem rekomendasi untuk mendapatkan nilai prediksi dari item oleh user aktif. Dalam collaborative filtering terdapat beberapa masalah yang sering muncul salah satunya adalah data sparsity. Data sparsity adalah permasalahan keterbatasan user untuk memberikan rating terhadap keseluruhan item. Untuk memecahankan masalah tersebut terdapat beberapa model diantaranya memory-based, model-based dan hybrid model. Tiap-tiap model memiliki kelebihan dan kekurangan masing-masing. Oleh karena itu, dalam tugas akhir ini digunakan model gabungan yaitu menggabungkan clustering dan smoothing untuk menangani masalah data sparsity dalam prediksi rating user. Dari hasil pengujian didapatkan nilai Mean Absolute Error (MAE) terkecil sebesar 0,732. Kata kunci : collaborative filtering, sparsity, clustering, smoothin

Automatic tracking and control for web recommendation New approaches for web recommendation

International audienceRecommender systems provide users with pertinent resources according to their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long distance resources in the history. Our main idea to solve this problem is the following: we consider that users browsing web pages or web contents can be seen as objects moving along trajectories in the web space. Having this assumption, we derive the appropriate description of the so-called recommender space to propose a mathematical model describing the behavior of the users/targets in the web/along the trajectories inside the recommender space. The second main assumption can then be expressed as follow: if we are able to track the users/targets along their trajectories, we are able to predict the future positions in the sub-spaces of the recommender space i.e., we are able to derive a new method for web recommendation and behavior monitoring. To achieve these objectives, we use the theory of the dynamic state estimation and more specifically the theory of Kalman filtering. We establish the appropriate model of the target tracker and we derive the iterative formulation of the filter. Then, we propose a new recommender system formulated as a control loop. We validate our approach on data extracted from online video consumption and we derive a users monitoring approach. Conclusions and perspectives are derived from the analysis of the obtained results and focus on the formulation of a topology of the recommender space

The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications

The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data

Artificial Intelligence for Online Review Platforms - Data Understanding, Enhanced Approaches and Explanations in Recommender Systems and Aspect-based Sentiment Analysis

The epoch-making and ever faster technological progress provokes disruptive changes and poses pivotal challenges for individuals and organizations. In particular, artificial intelligence (AI) is a disruptive technology that offers tremendous potential for many fields such as information systems and electronic commerce. Therefore, this dissertation contributes to AI for online review platforms aiming at enabling the future for consumers, businesses and platforms by unveiling the potential of AI. To achieve this goal, the dissertation investigates six major research questions embedded in the triad of data understanding of online consumer reviews, enhanced approaches and explanations in recommender systems and aspect-based sentiment analysis