Search results for "Recommender system"

A Context-Aware Mobile Solution for Assisting Tourists in a Smart Environment

2017

Context-Awareness in Ensemble Recommender System Framework

2021

Recommender systems that provide recommendations based uniquely on information over users and items may not be very accurate in some situations. Therefore, adding contextual information to recommendations may be a good choice resulting in a system with increased precision. In an early work, we proposed an Ensemble Variational Autoencoders (EnsVAE) framework for recommendation. EnsVAE is adjusted to output interest probabilities by learning the distribution of each item's ratings and attempts to provide diverse novel items that are pertinent to users. In this paper, we propose and investigate a context awareness framework based on the Ensemblist Variational Autoencoders model with integratin…

Listwise Recommendation Approach with Non-negative Matrix Factorization

2018

Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predi…

Vectors of Pairwise Item Preferences

2019

Neural embedding has been widely applied as an effective category of vectorization methods in real-world recommender systems. However, its exploration of users’ explicit feedback on items, to create good quality user and item vectors is still limited. Existing neural embedding methods only consider the items that are accessed by the users, but neglect the scenario when a user gives high or low rating to a particular item. In this paper, we propose Pref2Vec, a method to generate vector representations of pairwise item preferences, users and items, which can be directly utilized for machine learning tasks. Specifically, Pref2Vec considers users’ pairwise item preferences as elementary units. …

RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes

2015

Abstract Recommender systems are used to provide filtered information from a large amount of elements. They provide personalized recommendations on products or services to users. The recommendations are intended to provide interesting elements to users. Recommender systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This paper proposes a recommender system in the leisure domain, specifically in the movie showtimes domain. The system proposed is called RecomMetz, and it is a context-aware mobile recommender system based on Semantic Web technologies. In detail, a domain ontology primarily…

Interactive Decision Aids

2011

Decision support systems assist people in making a decision or choosing a course of action in a nonroutine situation that requires judgment (Haubl and Trifts 2000; Kasper 1996). In online webstores, vendors can easily offer highly interactive types of decision support. These co-called interactive decision aids (IDA) “help consumers in making informed purchase decisions amidst the vast availability of online product offerings” (Wang and Benbasat 2009, p. 3). However, the application of IDA is not restricted to purchase decisions. They are general enough to be of use in any kind of choice task where alternatives are known.

A Hybrid Recommender System for Cultural Heritage Promotion

2021

Assisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularit…

Cost-Oriented Recommendation Model for E-Commerce

2012

Contemporary Web stores offer a wide range of products to e-customers. However, online sales are strongly dominated by a limited number of bestsellers whereas other, less popular or niche products are stored in inventory for a long time. Thus, they contribute to the problem of frozen capital and high inventory costs. To cope with this problem, we propose using information on product cost in a recommender system for a Web store. We discuss the proposed recommendation model, in which two criteria have been included: a predicted degree of meeting customer’s needs by a product and the product cost.

ONLINE RECOMMENDATION SYSTEMS’ USAGE BY COMPANIES IN BALTIC COUNTRIES

2017

Global retailers are using sophisticated online recommendation systems (ORS) which enhance customers' loyalty towards the specific site. Online markets in Baltic countries are growing fast, but Baltic e-commerce sites are not using a wide enough range of eWOM tools. The aim of this paper is – to evaluate how eWOM through ORS is perceived and used by digital marketing specialists and e-commerce players in the Baltics. Research methods used were: literature analysis on ORS’s influence on consumer purchase decisions, and an expert survey and monitoring study. The research results revealed major barriers for advanced ORS usage in the Baltics, as they discovered a gap between experts' opinion an…

Entity Recommendation for Everyday Digital Tasks

2021

| openaire: EC/H2020/826266/EU//CO-ADAPT Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitor…