Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

The Art and Science of Negotiation

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Lecture notes in business information processing

In this paper we present a novel technique for predicting the tastes of users in recommender systems based on collaborative filtering. Our technique is based on factorizing the rating matrix into two non negative matrices whose components lie within the range 0, 1 with an understandable probabilistic meaning. Thanks to this decomposition we can accurately predict the ratings of users, find out some groups of users with the same tastes, as well as justify and understand the recommendations our technique provides.

A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model

As surveillance becomes ubiquitous, the amount of data to be processed grows along with the demand for manpower to interpret the data. A key goal of surveillance is to detect behaviors that can be considered anomalous. As a result, an extensive body of research in automated surveillance has been developed, often with the goal of automatic detection of anomalies. Research into anomaly detection in automated surveillance covers a wide range of domains, employing a vast array of techniques. This review presents an overview of recent research approaches on the topic of anomaly detection in automated surveillance. The reviewed studies are analyzed across five aspects: surveillance target, anomaly definitions and assumptions, types of sensors used and the feature extraction processes, learning methods, and modeling algorithms.

A Review of Anomaly Detection in Automated Surveillance

Past years have witnessed a growing interest in e-commerce as a strategy for improving business. Several paradigms have arisen from the e-commerce field in recent years which try to support different business activities, such as B2C and C2C. This paper introduces a prototype of e-commerce portal, called e-Zoco, of which main features are: (i) a catalogue service intended to arrange product categories hierarchically and describe them through sets of attributes, (ii) a product selection service able to deal with imprecise and vague search preferences which returns a set of results clustered in accordance with their potential relevance to the user, and (iii) a rule-based knowledge learning service to provide the users with knowledge about the existing relationships among the attributes that describe a given product category. The portal prototype is supported by a multi-agent infrastructure composed of a set of agents responsible for providing these and other services.

A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals

A supervised learning approach to automate the acquisition of knowledge in surveillance systems

Users of B2C (Business-to-Consumer) portals often lack of detailed knowledge about the state of the market related to the product they want to purchase. This leads to consumers purchasing the most popular product of a category, although perhaps that is neither the best suited for their requirements nor at the best cost. In this paper, a methodology to develop a recommender system is proposed. Our proposal is based on a supervised learning approach to infer knowledge that allows consumers to unveil what the existing patterns among the features that describe the searched product are. Such knowledge allows consumers to learn what they can buy and at what cost.

A Recommender System Based on a Machine Learning Algorithm for B2C Portals

E-commerce became very popular in recent years because of the great success achieved by some applications such as online auctions. However, currently the trend is that users of these portals execute more transactions in direct shopping than in online auctions and, probably because of the beginning of many of such portals as auctioning sites, their effectiveness decreases when searching for products in the direct shopping context. In this paper, we introduce a usability-improved fuzzy searching mechanism that allows the specification of uncertain and vague searching preferences to return more relevant results in direct shopping.

Improving searching usability in direct shopping portals

Machine learning algorithms can be used to discover patterns in data into e-commerce C2C portals and this knowledge can then be useful to help customers to refine their searches and to choose what to buy. In this work, it is proposed a new machine learning algorithm based on fuzzy logic. We describe the proposed algorithm for supporting customer searches in e-marketplaces and show the results obtained on simulated examples.

L. M. Lopez-Lopez

Papers

A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals

A supervised learning approach to automate the acquisition of knowledge in surveillance systems

A Recommender System Based on a Machine Learning Algorithm for B2C Portals

Improving searching usability in direct shopping portals

Supporting Customer Searches in E-marketplaces by Means of Fuzzy Logic-Based Machine Learning