Feature-blind fairness in collaborative filtering recommender systems

Knowledge and Information Systems - Recommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate suggestions....     

Property-based collaborative filtering for health-aware recommender systems

Recommender systems aim at solving the problem of information overload by selecting items (commercial products, educational assets, TV programs, etc.) that match the consumers’ interests and preferences. Recently, there have been approaches to drive the recommendations by the information stored in electronic health records, for which the traditional strategies applied in online shopping, e-learning, entertainment and other areas have several pitfalls. This paper addresses those problems by introducing a new filtering strategy, centered on the properties that characterize the items and the users. Preliminary experiments with real users have proved that this approach outperforms previous ones in terms of consumers’ satisfaction with the recommended items. The benefits are especially apparent among people with specific health concerns.     

A framework for collaborative filtering recommender systems

As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users’ trust in these. This paper provides: (a) measures to evaluate the novelty of the users’ recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust.     

Neural embedding collaborative filtering for recommender systems

Neural Computing and Applications - The main purpose of collaborative filtering algorithm is to provide a personalized recommender system based on past interactions of each user (e.g., clicks and...     

基于项的协同过滤在推荐系统中的应用研究

分析基于项的协同过滤在推荐系统中应用及所存在的问题,提出了一个基于项的协同过滤改进算法,并给出了改进算法在标准数据集上的实验结果,对改进算法与原算法进行了相关性能的比较分析,证明了改进算法的有效性.最后,对研究进行了总结,指出存在的不足,提出了进一步研究的方向.     

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.     

Biclustering neighborhood-based collaborative filtering method for top-n recommender systems

Knowledge and Information Systems - We propose a novel collaborative filtering method for top- $$n$$ recommendation task using bicustering neighborhood approach. Our method takes advantage of local...     

An iterative semi-explicit rating method for building collaborative recommender systems

Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only.     

Two collaborative filtering recommender systems based on sparse dictionary coding

This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user’s future rating of a specific item. Secondly, a top-n recommender system which finds a list of items predicted to be most relevant for a given user. The proposed methods are assessed using a variety of different metrics and are shown to be competitive with existing collaborative filtering recommender systems. Specifically, the sparse dictionary-based predictive recommender has advantages over existing methods in terms of a lower computational cost and not requiring parameter tuning. The sparse dictionary-based top-n recommender system has advantages over existing methods in terms of the accuracy of the predictions it makes and not requiring parameter tuning. An open-source software implemented and used for the evaluation in this paper is also provided for reproducibility.     

蚁群算法在协同过滤推荐系统中的应用研究

本文在CF的基础上重点研究推荐系统中的相似兴趣用户的聚类技术.研究重点集中在聚类方法的可伸缩性、聚类复杂形状和数据的有效性,分析了蚂蚁算法的特性和基于蚂蚁聚类的协同过滤推荐系统,并通过实验测试,表明使了利用蚂蚁的聚类特性能降低推荐系统计算量,从而提高系统的伸缩性.     

A new collaborative filtering metric that improves the behavior of recommender systems

Recommender systems are typically provided as Web 2.0 services and are part of the range of applications that give support to large-scale social networks, enabling on-line recommendations to be made based on the use of networked databases. The operating core of recommender systems is based on the collaborative filtering stage, which, in current user to user recommender processes, usually uses the Pearson correlation metric. In this paper, we present a new metric which combines the numerical information of the votes with independent information from those values, based on the proportions of the common and uncommon votes between each pair of users. Likewise, we define the reasoning and experiments on which the design of the metric is based and the restriction of being applied to recommender systems where the possible range of votes is not greater than 5. In order to demonstrate the superior nature of the proposed metric, we provide the comparative results of a set of experiments based on the MovieLens, FilmAffinity and NetFlix databases. In addition to the traditional levels of accuracy, results are also provided on the metrics’ coverage, the percentage of hits obtained and the precision/recall.     

The impact of consumer preferences on the accuracy of collaborative filtering recommender systems

Despite the omnipresent use of recommender systems in electronic markets, previous research has not analyzed how consumer preferences affect the accuracy of recommender systems. Markets, however, are characterized by a certain structure of consumers’ preferences. Consequently, it is not known in which markets recommender systems perform well. In this paper, we introduce a microeconomic model that allows a systematical analysis of different structures of consumers’ preferences. We develop a model-specific metric to measure the recommendation accuracy. We employ our model in a simulation to evaluate the impact of the structure of the consumers’ preferences on the accuracy of a popular collaborative filtering algorithm. Our study shows that recommendation accuracy is significantly affected by the similarity and number of consumer types and the distribution of consumers. The investigation reveals that in certain markets even random product recommendations outperform the collaborative filtering algorithm.     

An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering

Recommender systems (RS) are often used as guides, helping users to discover products of their interest. Many techniques and approaches to generate an effective recommendation are available for the system designers. On the one hand, this is interesting because different application’s scenarios could have a fittest solution but on the other it can also cause some complexity to select the best technique to address at each state of the database. Thus, choose the best technique for each new state becomes too difficult and frequent for manually select. One of big challenges on RS is turn the techniques more useful for real-world scenarios. Therefore, automate or help the design decision is an important task to improve the usability of RS and reduce its cost. Although many works aims to improve the performance of RS for some scenarios, just a few of them try to help the designers on selection or combination of the techniques through applications’ state changes. Therefore, this work proposes an evolutionary approach, called Invenire, to automate the choice of techniques used by combining results of different recommendation techniques. This is a new approach that uses a search algorithm to optimize the techniques combination, and can inspire hybrid methods and expert systems on how automate them. To evaluate the proposal, experiments were performed with a dataset from MovieLens and different collaborative filtering approaches. The results obtained show that the Invenire outperforms all collaborative filtering approach separately in all contexts addressed. The improvement achieved varies from 3.6% to 118.99% depending on the combination encountered and the experiment executed. Thus, the proposal was able to increase the accuracy on the generated recommendations and automate the combinations of techniques.     

A domain model of Web recommender systems based on usage mining and collaborative filtering

Considering the increasing demand of multi-agent systems, the practice of software reuse is essential to the development of such systems. Multi-agent domain engineering is a process for the construction of domain-specific agent-based reusable software artifacts, like domain models, representing the requirements of a family of multi-agent systems in a domain, and frameworks, implementing reusable agent-based design solutions to those requirements. This article describes the domain modeling tasks of the MADEM methodology and a case study on the application of GRAMO, a MADEM technique, for the construction of the domain model of ONTOWUM, specifying the common and variable requirements of a family of Web recommender systems based on usage mining and collaborative filtering.     

Improvement of collaborative filtering using rating normalization

With the advent of the Internet, the types and amount of information one can access have increased dramatically. In today’s overwhelming information environment, recommendation systems that quickly analyze large amounts of available information and help users find items of interest are increasingly needed. This paper proposes an improvement of an existing preference prediction algorithm to increase the accuracy of recommendation systems. In a recommendation system, prediction of items preferred by users is based on their ratings. However, individual users with the same degree of satisfaction to an item may give different ratings to the item. We intend to make more precise preference prediction by perceiving differences in users’ rating dispositions. The proposed method consists of two processes of perceiving users’ rating dispositions with clustering and of performing rating normalization according to such rating dispositions. The experimental results show that our method yields higher performance than ordinary collaborative filtering approach.     

Using second-hand information in collaborative recommender systems

Building recommender systems (RSs) has attracted considerable attention in the recent years. The main problem with these systems lies in those items for which we have little information and which cause incorrect predictions. One accredited solution involves using the items’ content information to improve these recommendations, but this cannot be applied in situations where the content information is unavailable. In this paper we present a novel idea to deal with this problem, using only the available users’ ratings. The objective is to use all possible information in the dataset to improve recommendations made with little information. For this purpose we will use what we call second-hand information: in the recommendation process, when a similar user has not rated the target item, we will guess his/her preferences using the information available. This idea is independent from the RS used and, in order to test it, we will employ two different collaborative RS. The results obtained confirm the soundness of our proposal.     

聚类与协同过滤相结合的隐式推荐系统

针对目前大多推荐系统中使用的协同过滤算法都需要有显示的用户反馈的问题,提出一种在隐式反馈推荐系统中使用聚类与矩阵分解技术相结合的方法,为用户提供更好地推荐结果。其结果是由基于用户历史购买记录的隐式反馈产生的,不需任何显式反馈提供的数据。采用高维的、无参数的分裂层次聚类技术产生聚类结果,根据聚类的结果为每个用户提供高兴趣度的个性化推荐。实验结果表明,在隐式反馈的情况下该方法也能有效获得用户偏好,产生大量的高准确度推荐。     

CinemaScreen recommender agent: combining collaborative and content-based filtering

A film recommender agent expands and fine-tunes collaborative-filtering results according to filtered content elements - namely, actors, directors, and genres. This approach supports recommendations for newly released, previously unrated titles. Directing users to relevant content is increasingly important in today's society with its ever-growing information mass. To this end, recommender systems have become a significant component of e-commerce systems and an interesting application domain for intelligent agent technology.     

Iterative rating prediction for neighborhood-based collaborative filtering

This paper investigates the issue of rating prediction for neighborhood-based collaborative filtering in recommendation systems. A novel rating prediction algorithm, called iterative rating prediction (IRP), is proposed for neighborhood-based collaborative filtering. The main idea behind IRP is neighborhood propagation. To predict ratings of items for target users, IRP relies on not only the rating information of direct neighbors but also that of indirect neighbors with different propagation depth. To implement the idea, IRP iteratively updates the ratings of items for users. The efficiency of the proposed method is examined through extensive experiments. Experimental results demonstrate the superior performance of our method, especially on small-scaled and sparse datasets.

     

How scales influence user rating behaviour in recommender systems

Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.