Moreopt: A goal programming based movie recommender system

Inan, Emrah; Tekbacak, Fatih; Öztürk, Cemalettin

doi:10.1016/j.jocs.2018.08.004

Cited by 22 publications

(13 citation statements)

References 19 publications

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“…The experiment results demonstrated that the proposed method outperforms the traditional content-aware CF method. Inan et al [16] proposed to combine EMDP with a method proposed by Özbal et al [13] to achieve more efficient movie recommendations. Similar to [13], the proposed method was integrated with content information of the movies during the item similarity calculations.…”

Section: Related Studiesmentioning

confidence: 99%

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

et al. 2021

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Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

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Section: Related Studiesmentioning

confidence: 99%

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

et al. 2021

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“…Bougiatiotis and Giannakopoulos (2018) constructed a similarity matrix from movie content, such as textual, auditory and visual information. Inan et al (2018) utilized the most appropriate feature weight values, such as year, genre, to determine similarity between movies.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

Tang

Wang

Cui³

et al. 2021

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PurposeBecause of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).Design/methodology/approachBecause interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.FindingsThe IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.Originality/valueDifferent from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

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“…Personalized means give recommendations according to user's preferences such as: if a user wants to go to restaurant, he has to give his preferences about location, food and many more things beforehand for getting good recommendations of restaurants according to his taste [3]. In non-personalized recommendations, recommender systems provide recommendations according to the content: For example, news recommender system such as Google news, recommends users news similar to the news that user has been watching [4]. Recommendations are not something which user type in search engine and get the results; it is the search result which comes after matching the user's query.…”

Section: Introductionmentioning

confidence: 99%

User-Review Oriented Social Recommender System for Event Planning

Singh¹,

Singh²,

Bathla³

2020

ISI

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Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

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Moreopt: A goal programming based movie recommender system

Cited by 22 publications

References 19 publications

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

User-Review Oriented Social Recommender System for Event Planning

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