Clustering in Recommendation Systems Using Swarm Intelligence

Ένα σύστημα συστάσεων είναι μία εφαρμογή που εκμεταλλεύεται πληροφορίες για να βοηθήσει τους χρήστες στη λήψη αποφάσεων προτείνοντας αντικείμενα που μπορεί να τους αρέσουν. Ένα σύστημα συστάσεων που βασίζεται στην τεχνική του συνεργατικού φιλτραρίσματος (collaborative filtering) δημιουργεί συστάσεις στους χρήστες με βάση τις προτιμήσεις παρόμοιων χρηστών. Ωστόσο, αυτός ο τύπος συστήματος συστάσεων δεν είναι τόσο αποτελεσματικός όταν τα δεδομένα αυξάνονται σε μεγάλο βαθμό (scalability) ή όταν δεν υπάρχει αρκετή πληροφορία (sparsity), καθώς δεν ομαδοποιούνται σωστά οι παρόμοιοι χρήστες. Αυτή η διπλωματική εργασία προτείνει τρείς υβριδικούς αλγορίθμους που ο καθένας συνδυάζει τον αλγόριθμο k-means με έναν αλγόριθμο ευφυΐας σμήνους για να βελτιώσει την ομαδοποίηση των χρηστών, και κατ’ επέκταση την ποιότητα των συστάσεων. Οι αλγόριθμοι ευφυΐας σμήνους που χρησιμοποιούνται είναι o αλγόριθμος τεχνητής κοινωνίας μελισσών (artificial bee colony), ο αλγόριθμος βελτιστοποίησης αναζήτησης κούκων (cuckoo search optimization) και ο αλγόριθμος βελτιστοποίησης γκρίζων λύκων (grey-wolf optimization). Οι προτεινόμενες μέθοδοι αξιολογήθηκαν χρησιμοποιώντας ένα σύνολο δεδομένων του MovieLens. Η αξιολόγηση δείχνει πως τα προτεινόμενα συστήματα συστάσεων αποδίδουν καλύτερα σε σύγκριση με τις ήδη υπάρχουσες τεχνικές όσον αφορά τις μετρικές του μέσου απόλυτου σφάλματος (mean absolute error - MAE), της ακρίβειας (precision), του αθροίσματος των τετραγωνικών σφαλμάτων (sum of squared errors - SSE) και της ανάκλησης (recall). Επιπλέον, τα αποτελέσματα της αξιολόγησης δείχνουν πως ο υβριδικός αλγόριθμος που χρησιμοποιεί την μέθοδο της τεχνητής κοινωνίας μελισσών αποδίδει ελαφρώς καλύτερα από τους άλλους δύο προτεινόμενους αλγορίθμους.A recommender system (RS) is an application that exploits information to help users in decision making by suggesting items they might like. A collaborative recommender system generates recommendations to users based on their similar neighbor’s preferences. However, this type of recommender system faces the data sparsity and scalability problems making the neighborhood selection a challenging task. This thesis proposes three hybrid collaborative recommender systems that each one combines the k-means algorithm with a different bio-inspired technique to enhance the clustering task, and therefore to improve the recommendation quality. The used bio-inspired techniques are artificial bee colony (ABC), cuckoo search optimization (CSO), and grey-wolf optimizer (GWO). The proposed approaches were evaluated over a MovieLens dataset. The evaluation shows that the proposed recommender systems perform better compared to already existing techniques in terms of mean absolute error (MAE), precision, sum of squared errors (SSE), and recall. Moreover, the experimental results indicate that the hybrid recommender system that uses the ABC method performs slightly better than the other two proposed hybrid algorithms

Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System

Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset

‎Provenance Based Trust Boosted Recommender System Using Boosted Vector Similarity Measure

‎As users in an online social network are overwhelmed by the abundant amount of information‎, ‎it is very hard to retrieve the preferred or required content‎. ‎In this context‎, ‎an online recommender system helps to filter and recommend content such as people,items or services‎. ‎But‎, ‎in a real scenario‎, ‎people rely more on recommendations‎ ‎from trusted sources than distrusting sources‎. ‎Though‎, ‎there are many trust based recommender systems that exist‎, ‎it lag in prediction error‎. ‎In order to improve the accuracy of the prediction‎, ‎this paper proposes a Trust-Boosted Recommender System (TBRS)‎. ‎Since‎, ‎the provenance derives the trust in a better way than other approaches‎, ‎TBRS is built‎ ‎from the provenance concept‎. ‎The proposed recommender system takes the provenance based fuzzy rules which were derived from the Fuzzy Decision Tree‎. ‎TBRS then computes the multi-attribute vector similarity score and boosts the score with trust weight‎. ‎This system is tested on the book-review dataset to recommend the top-k trustworthy reviewers.The performance of the proposed method is evaluated in terms of MAE and RMSE‎. ‎The result shows that the error value of boosted similarity is lesser than without boost‎. ‎The reduced error rates of the Jaccard‎, ‎Dice and Cosine similarity measures are 18\%‎, ‎15\% and 7\% respectively‎. ‎Also‎, ‎when the model is subjected to failure analysis‎, ‎it gives better performance for unskewed data than slewed data‎. ‎The models fbest‎, ‎average and worst case predictions are 90\%‎, ‎50\% and $<$23\% respectively‎

Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items

In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively

A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation

Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method

Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems