Search results for "collaborative filtering"

Ranking-Oriented Collaborative Filtering: A Listwise Approach

2016

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in bot…

SCCF Parameter and Similarity Measure Optimization and Evaluation

2019

Neighborhood-based Collaborative Filtering (CF) is one of the most successful and widely used recommendation approaches; however, it suffers from major flaws especially under sparse environments. Traditional similarity measures used by neighborhood-based CF to find similar users or items are not suitable in sparse datasets. Sparse Subspace Clustering and common liking rate in CF (SCCF), a recently published research, proposed a tunable similarity measure oriented towards sparse datasets; however, its performance can be maximized and requires further analysis and investigation. In this paper, we propose and evaluate the performance of a new tuning mechanism, using the Mean Absolute Error (MA…

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…

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…

INTACMATO: An IIMT-Prototype

2011

In the previous chapter, we pointed out that although users evaluate IIMT very positively, only a limited number of IIMT are offered. We assume that the main reason for this is that online sellers do not know which IIMT they should offer and what they should exactly look like. We address these two aspects in this chapter. Firstly, we review literature on IDA in the field of human interaction. We discuss several drawbacks of current approaches as well as the resulting requirements for the design of IIMT. Secondly, we break down observed decision-making behavior into typical steps decision makers apply in their decision processes. These steps indicate which IIMT would offer appropriate decisi…

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

2017

Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. Ac…

2020

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the input effectively. However, the previous variants of VAE have so far seen limited application to domain-specific recommendations that require additional side information. Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. This architecture specifies a procedure to transform sub-recommenders’ predicted utility matrix into interest probabilities that allo…

An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal

2007

This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal Infoville XXI (http://www…

Kolaboratīvā filtrēšana ieteikumu sistēmās

2021

Darbs bija veltīts kolaboratīvai filtrēšanai ieteikumu sistēmās. Tika raksturota kolaboratīvās filtrēšanas metode, apskatīti galvēnie izaicinājumi, piemērām, datu nepietiekamība, mērogojamība u.c.. Sīkāk tika apskatīta uz atmiņu balstītas kolaboratīvās filtrēšanas metodes, uz modeļiem balstītas kolaboratīvās filtrēšanas metodes, hibrīdas kolaboratīvās filtrēšanas metodes un kolaboratīvās filtrēšanas novērtēšanas metrika. Praktiski tika apskatīts datu piemērs ar uz saturu balstītiem ieteikumiem un uz atmiņu balstītam kolaboratīvās filtrēšanas metodēm.

A Pseudo-Supervised Approach to Improve a Recommender Based on Collaborative Filtering

2003

This PhD Thesis develops an optimal recommender. First of all, users accessing to a Web site are clustered. If a user belongs to a cluster, the system offers services which are usually accessed by users from the same cluster in a collaborative filtering scheme. A novel approach based on a users simulator and a dynamic recommendation system is proposed. The simulator is used to create the situations that one can find in a Web site. Introduction of dynamics in the recommender allows to change the clusters and in turn, the decisions which are taken. Since the system is based both on supervised and unsupervised learning whose borders are not too clear in our approach, we talk about a pseudo-sup…