Following Good Examples - Health Goal-Oriented Food Recommendation based on Behavior Data
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Typical recommender systems try to mimic the past behaviors of users to make future recommendations. For example, in food recommendations, they tend to recommend the foods the user prefers. While the recommended foods may be easily accepted by the user, it cannot improve the user's dietary habits for a specific goal such as weight control. In this paper, we build a food recommendation system that can be used on the web or in a mobile app to help users meet their goals on body weight, while also taking into account their health information (BMI) and the nutrition information of foods (calories). Instead of applying dietary guidelines as constraints, we build recommendation models from the successful behaviors of comparable users: the weight loss model is trained using the historical food consumption data of similar users who successfully lost weight. By combining such a goal-oriented recommendation model with a general model, the recommendations can be smoothly tuned toward the goal without disruptive food changes. We tested the approach on real data collected from a popular weight management app. It is shown that our recommendation approach can better predict the foods for test periods where the user truly meets the goal, than the typical existing approaches.Keywords:
Consumption
Personalized recommendation systems can help people find things that interest them and are widely used in developing the Internet or e-commerce.Collaborative filtering (CF) seems to be the most popular technique in recommender systems.However, CF is weak in the process of finding similar users.To resolve these problems, trust-aware recommender systems (TaRSs) have been developed in recent years.In this study, we propose a new approach that incorporates the content of reviews in a TaRS.In addition, we use a new dataset that is collected from the Yahoo!Movie website, whereas traditional research has used Epinions or Movielens.Finally, we evaluate the experiment results using precision and coverage.
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Recommender systems are becoming increasingly popular with the evolution of the Internet,and collaborative filtering(CF) is one of the most important technologies in recommender systems.The performance of CF systems degrades with increasing number of customers and items.So,a new multiple-level user similarity is presented,which not only overcomes the difficulty of data sparsity,but also solves the similar but not same problem.The experimental results show that the presented algorithm can improve the performance of CF systems in both the recommendation quality and efficiency.
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The rush to purchase the latest products sometimes prevents people from thinking things through completely.Consequently, recommender services are increasingly emerging.By looking at industry trends, interviewing dozens of leading industry stakeholders, and using publicly available information, it is important to filter out the most relevant information for consumer electronics before purchasing their items.This paper presents an electronic product recommender system based on contextual information from sentiment analysis.The recommendation algorithms mostly rely on users' rating to make prediction of items.Such ratings are usually insufficient and very limited.We present a contextual information sentiment based model for recommender system by making use of user comments and preferences to provide a recommendation.The purpose of this approach is to avoid term ambiguity which is so called domain sensitivity problem in recommendation.The proposed contextual information sentiment-based model illustrates better performance by using results of RMSE and MAE measurements as compared to the conventional collaborative filtering approach in electronic product recommendation.
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Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results.
Educational Data Mining
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A Visited Item Frequency Based Recommender System: Experimental Evaluation and Scenario Description.
There has been a continuous development of new clustering and prediction techniques that help customers select products that meet their preferences and/or needs from an overwhelming amount of available choices. Because of the possible huge amount of available data, existing Recommender Systems showing good results might be difficult to implement and may require a lot of computational resources to perform in this scenario. In this paper, we present a more simple recommender system than the traditional ones, easy to implement, and requiring a reasonable amount of resources to perform. This system clusters users according to the frequency an item has been visited by users belonging to the same cluster, performing a collaborative filtering scheme. Experiments were conducted to evaluate the accuracy of this method using the Movielens dataset. Results obtained, as measured by the F-measure value, are comparable to other approaches found in the literature which are far more complex to implement. Following this, we explain the application of this system to an e-content site scenario for advertising. In this context, a filtering tool is shown which has been developed to filter and contextualize recommended items.
MovieLens
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After studying the Data Mining technique and the E-commerce Recommender System, this paper designs and presents a book recommender system based on the clustering algorithm and applies it to net-bookstores. This system clusters all the resources first before looking for near neighbors to narrow the searching scope, and therefore greatly promotes the efficiency of the system and achieves good recommending results in its application.
Scope (computer science)
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The application of personalized recommendation in the Internet effectively improved its service,especially the service of E-commerce.content-based filtering E-commerce recommender system was discussed fully in this paper.Users' unique features can be explored by means of vector space model(VSM) firstly.Then based on the qualitative value of products information,the recommender lists were obtained.Since the system can adapt to the users' feedback automatically,its performance were improved comprehensively.According to the experiments result,the overall performance of the recommender based on content-based filtering was enhanced with time.
Vector space model
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