588 research outputs found
A model for mobile content filtering on non-interactive recommendation systems
To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
A Hybrid Recommendation System Based on Association Rules
Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small number of items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users
Using association rule mining to enrich semantic concepts for video retrieval
In order to achieve true content-based information retrieval on video we should analyse and index video with
high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date,
etc. However the range of such high-level semantic concepts, detected either manually or automatically,
usually limited compared to the richness of information content in video and the potential vocabulary of
available concepts for indexing. Even though there is work to improve the performance of individual concept
classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences
are available to us. We describe in this paper our method for using association rule mining to automatically
enrich the representation of video content through a set of semantic concepts based on concept co-occurrence
patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts
of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach
A COLLABORATIVE FILTERING APPROACH TO PREDICT WEB PAGES OF INTEREST FROMNAVIGATION PATTERNS OF PAST USERS WITHIN AN ACADEMIC WEBSITE
This dissertation is a simulation study of factors and techniques involved in designing hyperlink recommender systems that recommend to users, web pages that past users with similar navigation behaviors found interesting. The methodology involves identification of pertinent factors or techniques, and for each one, addresses the following questions: (a) room for improvement; (b) better approach, if any; and (c) performance characteristics of the technique in environments that hyperlink recommender systems operate in. The following four problems are addressed:Web Page Classification. A new metric (PageRank × Inverse Links-to-Word count ratio) is proposed for classifying web pages as content or navigation, to help in the discovery of user navigation behaviors from web user access logs. Results of a small user study suggest that this metric leads to desirable results.Data Mining. A new apriori algorithm for mining association rules from large databases is proposed. The new algorithm addresses the problem of scaling of the classical apriori algorithm by eliminating an expensive joinstep, and applying the apriori property to every row of the database. In this study, association rules show the correlation relationships between user navigation behaviors and web pages they find interesting. The new algorithm has better space complexity than the classical one, and better time efficiency under some conditionsand comparable time efficiency under other conditions.Prediction Models for User Interests. We demonstrate that association rules that show the correlation relationships between user navigation patterns and web pages they find interesting can be transformed intocollaborative filtering data. We investigate collaborative filtering prediction models based on two approaches for computing prediction scores: using simple averages and weighted averages. Our findings suggest that theweighted averages scheme more accurately computes predictions of user interests than the simple averages scheme does.Clustering. Clustering techniques are frequently applied in the design of personalization systems. We studied the performance of the CLARANS clustering algorithm in high dimensional space in relation to the PAM and CLARA clustering algorithms. While CLARA had the best time performance, CLARANS resulted in clusterswith the lowest intra-cluster dissimilarities, and so was most effective in this regard
A fuzzy associative classification approach for recommender systems
Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative altering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations quality and eectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied eectively in recommender systems, minimizing the eects of typical drawbacks they present
IMPROVING RECOMMENDATION PERFORMANCE WITH USER INTEREST EVOLUTION PATTERNS
Effective recommendation is indispensable to customized or personalized services. Collaborative filtering approach is a salient technique to support automated recommendations, which relies on the profiles of customers to make recommendations to a target customer based on the neighbors with similar preferences. However, traditional collaborative recommendation techniques only use static information of customers’ preferences and ignore the evolution of their purchasing behaviours which contain valuable information for making recommendations. Thus, this study proposes an approach to increase the effectiveness of personalized recommendations by mining the sequence patterns from the evolving preferences of a target customer over time. The experimental results have shown that the proposed technique has improved the recommendation precision in comparison with collaborative filtering method based on Top k recommendation
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