485 research outputs found

    How to Retrain Recommender System? A Sequential Meta-Learning Method

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    Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

    BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation

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    Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation

    A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets

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    Advances in Big Data analytics and machine learning have offered intangible benefits across many areas of one’s health. One such area is a move towards healthier lifestyle choices such as one’s diet. Recommender systems apply techniques that can filter information and narrow that information down based on user preferences or user needs and help users choose what information is relevant. Commonly adopted across e-commerce sites, social networking and entertainment industries, recommender systems can also support nutrition-based health management, offering individuals more food options, not only based on one’s preferred tastes but also on one’s dietary needs and restrictions. This research presents the design, implementation and evaluation of three recommender systems using content-based, collaborative filtering and hybrid recommendation models within the nutrition domain

    Prediction of purchase behaviors across heterogeneous social networks

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    Recommender System Based on Semantic Similarity

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    In electronic commerce, in order to help users to find their favourite products, we essentially need a system to classify the products based on the user's interests and needs to recommend them to the users. For the same reason the recommendation systems are designed to help finding information in large websites. They are basically developed to offer products to the customers in an automated fashion to help them to do conveniently their shopping. The developing of such systems is important since there are often a large number of factors involved in purchasing a product that would make it difficult for the customer to make the best decision. Finding relationship among users and relationships among products are important issue in these systems. One of relations is similarity. Measure similarity among users and products is used in the pure methods for calculating similarity degree. In this paper, semantic similarity is used to find a set of k nearest neighbours to the target user, or target item. Thus, because of incorporating semantic similarity in the proposed recommendation system, from the experimental results, the high accuracy was obtained on private building company dataset in comparison with state-of-the-art recommender systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.393

    An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments

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    Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing studies have investigated the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not considered in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location-sensitive recommendation algorithm is then devised. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user's preference and location.This work was supported by the National Natural Science Foundation of China under Grant 61372187. G. Min’s work was partly supported by the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939. L. T. Yang is the corresponding author

    A Hybrid Recommendation System for Online Car Auction Platform and Product Review

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    Recommender systems are a relatively new field of interest in data science. There are roughly 2 approaches to recommender systems: content-based filtering and collaborative filtering. For this master’s paper substitute/project, I designed and implemented a hybrid recommendation system combining the two approaches. This document will detail the methods I used and the process of arriving at what I had. It will also include some of the obstacles I faced during the process, how I addressed them, and the ways the project changed because of it.Master of Science in Information Scienc
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