Design and evaluation of a movie recommendation system showing a review for evoking interested

2017 ◽  
Vol 13 (1) ◽  
pp. 72-84 ◽  
Author(s):  
Yuto Ishida ◽  
Takahiro Uchiya ◽  
Ichi Takumi
Keyword(s):  
Recommendation System ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Item Selection ◽  
Simple Method ◽  
Content Type ◽  
Goods And Services ◽  
Outcomes Evaluation ◽  
Movie Recommendation

Purpose In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference. Design/methodology/approach The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations. Findings Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user. Originality/value This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.

scholarly journals On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2138
Author(s):  
Sang-Min Choi ◽  
Dongwoo Lee ◽  
Chihyun Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Magic Number ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Minimum Number ◽  
Movie Recommendation ◽  
Content Based Filtering

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

Using latent features for building an interpretable recommendation system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziming Zeng ◽  
Yu Shi ◽  
Lavinia Florentina Pieptea ◽  
Junhua Ding
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
User Preference ◽  
Data Sets ◽  
Historical Records ◽  
Data Link ◽  
Content Type ◽  
Fine Grained ◽  
Public Data ◽  
Latent Features

Purpose Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation. Design/methodology/approach First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system Findings Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system. Originality/value This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

2020 ◽  
Vol 2 (95) ◽  
pp. 21-27
Author(s):  
S. F. Chalyi ◽  
V. O. Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Selection Process ◽  
Temporal Patterns ◽  
User Preferences ◽  
Data Sets ◽  
Goods And Services ◽  
Temporal Relationships ◽  
Sequential Selection ◽  
System User ◽  
Alternative Choice

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

A Particle Swarm Optimization based Hybrid Recommendation System

2016 ◽  
Vol 6 (2) ◽  
pp. 1-10
Author(s):  
Rabi Narayan Behera ◽  
Sujata Dash
Keyword(s):  
Recommendation System ◽  
Optimization Technique ◽  
User Profile ◽  
Matching Algorithm ◽  
Profile Matching ◽  
Machine Learning Approach ◽  
Movie Recommendation ◽  
Hybrid Recommendation ◽  
Content Based Filtering ◽  
Fine Tune

Due to rapid digital explosion user shows interest towards finding suggestions regarding a particular topic before taking any decision. Nowadays, a movie recommendation system is an upcoming area which suggests movies based on user profile. Many researchers working on supervised or semi-supervised ensemble based machine learning approach for matching more appropriate profiles and suggest related movies. In this paper a hybrid recommendation system is proposed which includes both collaborative and content based filtering to design a profile matching algorithm. A nature inspired Particle Swam Optimization technique is applied to fine tune the profile matching algorithm by assigning to multiple agents or particle with some initial random guess. The accuracy of the model will be judged comparing with Genetic algorithm.

scholarly journals Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenning Yuan ◽  
Jong Han Lee ◽  
Sai Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Preferences ◽  
Classification Model ◽  
Filtering Algorithm ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Low Dimensional ◽  
Preference Matrix

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

Usability testing for a community college library website

2019 ◽  
Vol 36 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Alyssa M. Valenti
Keyword(s):  
Web Design ◽  
Usability Testing ◽  
User Preferences ◽  
User Preference ◽  
Full Time ◽  
Content Type ◽  
Usability Tests ◽  
User Groups ◽  
Testing Case ◽  
First Time

Purpose This paper details a usability testing case study on a simplified homepage for [Library]. The usability testing was completed in Spring 2017 to meet the needs of diverse user groups and shifting trends in Web design and development. At the conclusion of the usability testing, recommendations for change informed the design decisions and a new homepage was implemented in October 2018. Design/methodology/approach The researcher performed eight usability tests with a combination of the different library user types; full-time faculty, students, an administrator and members of the public. The usability test consisted of 13 specific tasks. After testers completed the tasks, users filled out a 30-question Likert-scale questionnaire and answered a set of 8 open-ended questions. Findings This paper discusses the recommendations for change which the researcher discovered at the conclusion of the usability testing period. The research found the need to improve and include specific navigational, visual and easy-to-use elements to best meet the needs of the users in the usability tests. Changes were ranked and implemented on a scale of catastrophic to cosmetic. Research limitations/implications As websites, technology and user preferences continually evolve, the homepage will need to be tested for usability again in the next several years. Researchers are encouraged to adapt the methods to their own institutions. Practical implications This paper discusses findings specific to [Library], which in turn has proved to increase usage of certain features and functions by the user community. Originality/value This is the first time usability testing has been done for the [Library’s] website. It was the first time the design of the homepage was informed by real user preference. This paper is valuable to those looking to create a simple, easy-to-use homepage that best benefits their own unique community of users.

Effective user preference mining-based personalised movie recommendation system

2020 ◽  
Vol 13 (3) ◽  
pp. 371
Author(s):  
Marimuthu Karuppiah ◽  
Hamid Reza Karimi ◽  
D. Malathi ◽  
V. Vijayakumar ◽  
R. Logesh ◽  
...  
Keyword(s):  
Recommendation System ◽  
User Preference ◽  
Movie Recommendation

Modeling and quantifying uncertainty in the product design phase for effects of user preference changes

2015 ◽  
Vol 115 (9) ◽  
pp. 1637-1665 ◽  
Author(s):  
Hamid Afshari ◽  
Qingjin Peng
Keyword(s):  
Life Cycle ◽  
Product Design ◽  
Product Life Cycle ◽  
User Preferences ◽  
User Preference ◽  
Design Parameters ◽  
Accurate Estimation ◽  
Product Life ◽  
Content Type ◽  
External Uncertainty

Purpose – The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes. Design/methodology/approach – Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty. Findings – The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method. Practical implications – It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments. Originality/value – This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.

scholarly journals User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

2020 ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh Bharata Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
User Profile ◽  
Profile Data ◽  
Improve Performance ◽  
Similarity Algorithm ◽  
Single User ◽  
Previous Algorithm ◽  
Movie Recommendation

Abstract A recommendation system is a software used in the e-commerce field that provides recommendations for customers to choose the items they like. Several recommendation systems have been proposed; however, collaborative filtering is the most widely used approach. The main issue in collaborative filtering is how to implement a similarity algorithm that can improve performance in the recommendation system. Several similarity algorithms based on user rating value have been developed, and recently a similarity algorithm has been developed that combines the user rating value and the user behavior value. However, the existing research is still based only on a single user behavior value, which is the genre data. Therefore, we propose a new similarity algorithm that considers not only the genre data but also the user profile data (namely age, gender, occupation, and location). The new similarity we are proposing is called User Profile Correlation-based Similarity (UPCSim). The user profile correlation similarity was obtained by calculating the correlation coefficient between the user profile data and the user rating or behavior values. An experiment was done to compare the accuracy of the UPCSim algorithm with that of the previous algorithm. The experiment results show that the UPCSim algorithm can improve the recommendation performance MAE by 1.64% and RMSE by 1.4% compared to the previous algorithm.

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