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"Recommendation": models, code, and papers

Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach

Mar 23, 2022
Xiao Liu, Bonan Gao, Basem Suleiman, Han You, Zisu Ma, Yu Liu, Ali Anaissi

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this paper, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable IoT devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data and thus minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, weight. In particular, our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared to similar studies. Furthermore, our approach is novel compared to existing studies as it does not require collecting and using users sensitive information, and thus it preserves the users privacy.

* 30 pages, 16 figures, 36 references 

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Variational Inference for Category Recommendation in E-Commerce platforms

Apr 19, 2021
Ramasubramanian Balasubramanian, Venugopal Mani, Abhinav Mathur, Sushant Kumar, Kannan Achan

Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website. It is therefore important to surface precise and diverse category recommendations to aid the users' journey through the platform and to help them discover new groups of items. An often understated part in category recommendation is users' proclivity to repeat purchases. The structure of this temporal behavior can be harvested for better category recommendations and in this work, we attempt to harness this through variational inference. Further, to enhance the variational inference based optimization, we initialize the optimizer at better starting points through the well known Metapath2Vec algorithm. We demonstrate our results on two real-world datasets and show that our model outperforms standard baseline methods.

* 8 pages, 3 figures, 2 tables 

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Algorithmic clothing: hybrid recommendation, from street-style-to-shop

Nov 12, 2017
Y Qian, P Giaccone, M Sasdelli, E Vasquez, B Sengupta

In this paper we detail Cortexica's ( recommendation framework -- particularly, we describe how a hybrid visual recommender system can be created by combining conditional random fields for segmentation and deep neural networks for object localisation and feature representation. The recommendation system that is built after localisation, segmentation and classification has two properties -- first, it is knowledge based in the sense that it learns pairwise preference/occurrence matrix by utilising knowledge from experts (images from fashion blogs) and second, it is content-based as it utilises a deep learning based framework for learning feature representation. Such a construct is especially useful when there is a scarcity of user preference data, that forms the foundation of many collaborative recommendation algorithms.

* KDD 2017 Workshop on ML meets Fashion 

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Temporal Learning and Sequence Modeling for a Job Recommender System

Aug 11, 2016
Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, Prem Natarajan

We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.

* a shorter version in proceedings of RecSys Challenge 2016 

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Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems

Sep 09, 2021
Athanasios N. Nikolakopoulos, Xia Ning, Christian Desrosiers, George Karypis

Collaborative recommendation approaches based on nearest-neighbors are still highly popular today due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter offers a comprehensive survey of neighborhood-based methods for the item recommendation problem. It presents the main characteristics and benefits of such methods, describes key design choices for implementing a neighborhood-based recommender system, and gives practical information on how to make these choices. A broad range of methods is covered in the chapter, including traditional algorithms like k-nearest neighbors as well as advanced approaches based on matrix factorization, sparse coding and random walks.

* 50 pages; Chapter in the Recommender Systems Handbook, 3rd Edition (to appear) 

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Dual Attention Model for Citation Recommendation

Oct 01, 2020
Yang Zhang, Qiang Ma

Based on an exponentially increasing number of academic articles, discovering and citing comprehensive and appropriate resources has become a non-trivial task. Conventional citation recommender methods suffer from severe information loss. For example, they do not consider the section on which a user is working, the relatedness between words, or the importance of words. These shortcomings make such methods insufficient for recommending adequate citations when working on manuscripts. In this study, we propose a novel approach called dual attention model for citation recommendation (DACR) to recommend citations during manuscript preparation. Our method considers three dimensions of information: contextual words, structural contexts, and the section on which a user is working. The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information, and the latter aims to learn the importance of inputs. The experiments on real-world datasets demonstrate the effectiveness of the proposed approach.

* The 28th International Conference on Computational Linguistics (COLING2020) 

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Generative Interest Estimation for Document Recommendations

Nov 28, 2017
Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser

Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each user as a Gaussian mixture model (GMM). Recommendations can be obtained directly by sampling from a user's generative model. Using Latent semantic analysis (LSA) as comparison, we compute and explore document representations on the Delicious bookmarks dataset, a standard benchmark for recommender systems. We then perform density estimation in both spaces and show that learned representations outperform LSA in terms of predictive performance.

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Counterfactual Explanations for Neural Recommenders

May 11, 2021
Khanh Hiep Tran, Azin Ghazimatin, Rishiraj Saha Roy

Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem. Existing methods are usually based on attention distributions over a variety of features, which are still questionable regarding their suitability as explanations, and rather unwieldy to grasp for an end user. Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem. However, current work on such counterfactuals cannot be readily applied to neural models. In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. It extends recently-proposed influence functions for identifying training points most relevant to a recommendation, from a single to a pair of items, while deducing a counterfactual set in an iterative process. We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of the popular MovieLens 100K dataset.

* SIGIR 2021 Short Paper, 5 pages 

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On Affinity Measures for Artificial Immune System Movie Recommenders

May 16, 2008
Uwe Aickelin, Qi Chen

We combine Artificial Immune Systems 'AIS', technology with Collaborative Filtering 'CF' and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin 3, 4, 5. Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendalls Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.

* Proceedings of the 5th International Conference on Recent Advances in Soft Computing (RASC 2004), Nottingham, UK 

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A Graph-based Method for Session-based Recommendations

Jun 22, 2021
Marina Delianidi, Michail Salampasis, Konstantinos Diamantaras, Theodosios Siomos, Alkiviadis Katsalis, Iphigenia Karaveli

We present a graph-based approach for the data management tasks and the efficient operation of a system for session-based next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate without any excessive training phase. Our work aims at developing a recommender method that represents a balance between data processing and management efficiency requirements and the effectiveness of the recommendations produced. We use the Neo4j graph database to implement a prototype of such a system. Furthermore, we use an industry dataset corresponding to a typical e-commerce session-based scenario, and we report on experiments using our graph-based approach and other state-of-the-art machine learning and deep learning methods.

* Preprint version of the paper, the original paper is published on ACM DL. 6 pages, 1 figure, 1 table 

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