Incorporating user control into recommender systems based on naive bayesian classification

Abstract

Recommender systems are increasingly being employed to personalize services, such as on the web, but also in electronics devices, such as personal video recorders. These recommenders learn a user profile, based on rating feedback from the user on, e.g., books, songs, or TV programs, and use machine learning techniques to infer the ratings of new items.The techniques commonly used are collaborative filtering and naive Bayesian classification, and they are known to have several problems, in particular the cold-start problem and its slow adaptivity to changing user preferences. These problems can be mitigated by allowing the user to set up or manipulate his profile.In this paper, we propose an extension to the naive Bayesian classifier that enhances user control. We do this by maintaining and flexibly integrating two profiles for a user, one learned by rating feedback, and one created by the user. We in particular show how the cold-start problem is mitigated.

Recommendation and Classification Systems: A Systematic Mapping Study

“…Reference Building accurate and practical recommender system algorithms using machine learning classifier and collaborative filtering [20] DGA botnet detection using collaborative filtering and density-based clustering [21] A multistage collaborative filtering method for fall detection [22] Analysis and performance of collaborative filtering and classification algorithms [1] Extracting a vocabulary of surprise by collaborative filtering mixture and analysis of feelings [4] Content based filtering in online social network using inference algorithm [23] Building switching hybrid recommender system using machine learning classifiers and collaborative filtering [8] Imputation-boosted collaborative filtering using machine learning classifiers [24] CRISP-an interruption management algorithm based on collaborative filtering [25] A credit scoring model based on collaborative filtering [26] Collaborative filtering recommender systems [2] An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering [6] Tweet modeling with LSTM recurrent neural networks for hashtag recommendation [27] A two-stage cross-domain recommendation for cold start problem in cyber-physical systems [28] ELM based imputation-boosted proactive recommender systems [29] Twitter-user recommender system using tweets: a content-based approach [30] A personalized time-bound activity recommendation system [31] Automated content based short text classification for filtering undesired posts on Facebook [32] Shilling attack detection in collaborative recommender systems using a meta learning strategy [33] Building a distributed generic recommender using scalable data mining library [34] Context-aware movie recommendation based on signal processing and machine learning [35] Recommender systems using linear classifiers [36] A survey of accuracy evaluation metrics of recommendation tasks [3] Incorporating user control into recommender systems based on naive Bayesian classification [37] Classification features for attack detection in collaborative recommender systems [38] Automatic tag recommendation algorithms for social recommender systems [39] Optimizing similar item recommendations in a semistructured marketplace to maximize conversion …”

Survey on Recommendation System

“…Afterwards, using different evaluation, the weight are adjusted as per need. The example of such type of system is Ptango [22].…”

A literature review of recommender systems in the television domain

“…Regarding to the context of the studies, we observed the following result: academy (235 studies, 83%) and industry (47 studies, 17%). The sources with the greatest number of studies from Industry were ACM conference on Recommender systems with ten studies: (Pronk et al, 2007) (Antonelli and Francini, 2009) …”