Hybrid-ε-greedy for Mobile Context-aware Recommender System

In this demo paper we present a novel context-aware mobile recommender system for places of interest (POIs). Unlike existing systems, which learn users' preferences solely from their past ratings, it considers also their personality -using the Five Factor Model. Personality is acquired by asking users to complete a brief and entertaining questionnaire as part of the registration process, and is then exploited in:

International Journal of Computer Science and Information Technology Research, 2017

The quest to develop applications to fully assist humans in our daily activities through the acquisition and utilization of contextual information has been the ultimate concern of researchers in pervasive computing. Many different implementation mechanisms have been adopted over the years. Currently, researchers are focusing on artificial neural network to develop context-aware recommender systems. This paper examines the various architectures and learning algorithms employed in these systems in order to deduce the general trend of implementation whilst assessing their pros and cons through literature survey. The paper goes ahead to recommend robust mechanism for future applications that can be used in mobile environment. The work revealed that the general trend of implementation is through threshold scoring mechanisms, reliance on the internet, complicated learning algorithms and architectures with the view to achieving higher prediction accuracy. These mechanisms ignore the limited capabilities of mobile devices in achieving pervasive computing. The work therefore recommended that there is the need to use simple but adaptive neural network architectures and learning algorithms so that these systems can be implemented on mobile devices without any negative impact on them

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Recommender systems are faced with new challenges that are beyond traditional techniques. For example, most traditional techniques are based on similarity or overlap among existing data, however, there may not exist sufficient historical records for some new users to predict their preference, or users can hold diverse interest, but the similarity based methods may probably over-narrow it.

2010 10th International Conference on Intelligent Systems Design and Applications, 2010

Situation awareness is a promising approach to recommend to a mobile user the most suitable resources for a specific situation. However, determining the correct user situation is not a simple task since users have different habits that may affect the way in which the situations arise. Thus, an appropriate tuning aimed at adapting the situation recognizer to the specific user is desirable to make a resource recommender more effective. In this paper, we show how this objective can be achieved by collecting data during the interaction of the user with the mobile device and using this context history to personalize the resource recommender by a genetic algorithm. To describe our approach, we adopt a recently proposed resource recommender which exploits fuzzy linguistic variables to manage the inherent vagueness of some contextual parameters. Experimental results on a real business case show that the responsiveness and modeling capabilities of the recommender increase, thus validating the proposed approach.

Services Proceedings of the 2016 S2, 2016

With the rapid development of wireless communication and mobile internet, it is impossible to obtain high-quality recommendation just depending on traditional user-item binary relation. How to use multi-context to generate satisfying recommendation in such a complicated environment has been a hot pot in some fields like e-commerce, tourism, music and video. Context aware recommender system (CARS) import multi-context into recommender to generate recommendation with ubiquitousness and personalization. This paper presents an overview of the development status of CARS in recent years. We detail the definition of context, usage, process of CARS and evaluation. We also analyze some challengingresearch extension.

Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the coldstart phase, i.e., when no or few ratings are available for the items listed in the system catalogue and it is therefore hard to predict the missing ratings and compose relevant recommendations. This problem has not been directly tackled in previous research. Hence, in order to address it, we have designed and implemented several novel algorithmic components and interface elements in a fully operational points of interest (POI) mobile recommender system (STS). In particular, in this article we illustrate the benefits brought by using the user personality and active learning techniques. We have developed two extended versions of the matrix factorisation algorithm to identify what items the users could and should rate and to compose personalised recommendations. While context-aware recommender systems have been mostly evaluated offline, a testing scenario that suffers from many limitations, in our analysis we evaluate the proposed system in live user studies where the graphical user interface and the full interaction design play a major role. We have measured the system effectiveness in terms of several metrics such as: the quality and quantity of acquired ratings-in-context, the recommendation accuracy (MAE), the system precision, the perceived recommendation quality, the user choice satisfaction, and the system usability. The obtained results confirm that the proposed techniques can effectively overcome the identified cold-start problem.

Djallel Bouneffouf

Recently, researchers have started to model interactions between us-ers and search engines as an online learning ranking. Such systems obtain feed-back only on the few top-ranked documents results. To obtain feedbacks on other documents, the system has to explore the non-top-ranked documents that could lead to a better solution. However, the system also needs to ensure that the quality of result lists is high by exploiting what is already known. Clearly, this results in an exploration/exploitation dilemma. We introduce in this paper an algorithm that tackles this dilemma in Context-Based Information Retrieval (CBIR) area. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed online framework we conduct evaluations with mobile users. The experimental results demon-strate that our algorithm outperforms surveyed algorithms.

IRJET, 2020

This is innovative E-commerce recommending software for user and stakeholder. As in general recommendation engine only aims at optimizing user but not the stakeholders. In this appear we make attempt that satisfy the needs of both user and stakeholders. In this project we use the Reinforcement mechanism and Naive Bayesian probability to seek the subclass of information and by applying the algorithms such as clustering and filtering we recommend the products to user based on the fairness and relevance approach. It helps the user to take correct decision in their browsing experience, increase sales and online transaction.

Computing, 2015

The demand for ubiquitous information processing over the Web has called for the development of context-aware recommender systems capable of dealing with the problems of information overload and information filtering. Contemporary recommender systems harness context-awareness with the personalization to offer the most accurate recommendations about different products, services, and resources. However, such systems come across the issues, such as sparsity, cold start, and scalability that lead to imprecise recommendations. Computational Intelligence (CI) techniques not only improve recommendation accuracy but also substantially mitigate the aforementioned issues. Large numbers of context-aware recommender systems are based on the CI techniques, such as: (a) fuzzy sets, (b) Artificial Neural Networks (ANNs), (c) Evolutionary Computing (EC), (d) Swarm Intelligence (SI), and (e) Artificial Immune Systems (AIS). This survey aims to encompass the state-of-the-art context-aware recommender systems based on the CI techniques. Taxonomy of the CI techniques is presented and challenges particular to the context-aware recommender systems are also discussed. Moreover, the ability of each of the CI techniques to deal with the aforesaid challenges is also highlighted. Furthermore, the strengths and weaknesses of each of the CI techniques used in context-aware recommender systems are discussed and a comparison of the techniques is also presented.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context i...