Movie recommendation based on bridging movie feature and user interest

Li, Jing; Xu, Wentao; Wan, Wenbo; Sun, Jiande

doi:10.1016/j.jocs.2018.03.009

Cited by 42 publications

(17 citation statements)

References 19 publications

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“…A hybrid recommendation algorithm for bridging the movie feature and user interest for recommending movies to the user using the MovieLens dataset [ 32 ].…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

“…A hybrid recommendation algorithm is proposed in [ 32 ] which integrates movie feature and user interest for the similarity calculation between users, using the collaborative filtering approach for movies recommendation when user-item matrix is sparse. Experiments are performed on only one dataset of MovieLens from the year 1998 using one evaluation metric of MAE (Mean Absolute Error).…”

Section: Related Workmentioning

confidence: 99%

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HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

2018

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A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online. In an effort to overcome the data sparsity issue in recommender systems, this research incorporates a content based filtering technique with fuzzy inference system and a conformal prediction approach introducing a new framework called Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). The proposed framework is implemented to be used in the domain of movies and it provides quality recommendations to users with a confidence level and an improved accuracy. In our proposed framework, first, a Content Based Filtering (CBF) technique is applied to create a user profile by considering the history of each user. CBF is useful in the situations like: lack of demographic information and the data sparsity problems. Second, a Fuzzy based technique is incorporated to find the similarities and differences between the user profile and the movies in the dataset using a set of fuzzy rules to get a predicted rating for each movie. Third, a Conformal prediction algorithm is implemented to calculate the non-conformity measure between the predicted ratings produced by fuzzy system and the actual ratings from the dataset. A p-value (confidence measure) is computed to give a level of confidence to each recommended item and a bound is set on the confidence level called a significance level ε, according to which the movies only above the specified significance level are recommended to user. By building a confidence centric hybrid conformal recommender system using the content based filtering approach with fuzzy logic and conformal prediction algorithm, the reliability and the accuracy of the system is considerably enhanced. The experiments are evaluated on MovieLens and Movie Tweetings datasets for recommending movies to the users and they are compared with other state-of-the-art recommender systems. Finally, the results confirm that the proposed algorithms perform better than the traditional ones.

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“…A hybrid recommendation algorithm for bridging the movie feature and user interest for recommending movies to the user using the MovieLens dataset [ 32 ].…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

2018

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“…[24][25][26] Feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and nonredundant, facilitating the subsequent learning and generalization steps and in some cases leading to better human interpretations. [27][28][29][30] Song first used K-means clustering to segment overlapping apple images and processed the morphological processing of the target fruit to extract the continuous smooth contour curve of the convex hull of the target fruit edge. The center and radius of the contour curve were calculated to identify and locate overlapping fruits.…”

Section: Overlap or Shading Fruit Processingmentioning

confidence: 99%

Apple harvesting robot under information technology: A review

Jia

Yan

Lian

et al. 2020

International Journal of Advanced Robotic Systems

103

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It has been more than 30 years since the French pioneered the research of the apple harvesting robot, and with the joint efforts of scholars all around the world, a variety of prototypes have been developed. However, the existing apple harvesting robot prototype is still in the experimental research stage because of its low harvesting efficiency. With the help of information technology, the related research has ushered in a milestone development, and it is full of opportunities and challenges for apple harvesting robotic researchers. In this article, it briefly introduced the development history, structure, and composition of apple harvesting robots and the operation process, which makes readers have a clear understanding of apple harvesting robot and its harvesting principle. Then systematically summarizing the research results of apple harvesting robots both at domestic and at foreign, we carried out in following three aspects: rapid and accurate recognition and positioning of target fruit, all-weather operation mode, and application of intelligent computing theory in apple harvesting robots, and it analyzes the research progress of apple harvesting robot in detail. The results show that improving the harvesting efficiency is the key and hot spot for the research on apple harvesting robots. Under the impetus of information technology, how to achieve fast and accurate recognition of the fruits of multienvironment and multiple information, obtain reasonable path planning, and further optimization of control strategies are all important research directions.

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“…Logesh et al [26] proposed an activity and behaviour induced personalized RS to predict persuasive POI recommendations. Li et al [27] used movie feature vector combined with the user rating matrix to generate the user interest vector. Tahmasbi et al [28] gave an approach to model temporal dynamics of user preferences in movie recommendation systems based on a coupled tensor factorization framework.…”

Section: Related Workmentioning

confidence: 99%

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Kumar

Al‐Turjman³

et al. 2020

IEEE Access

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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

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Movie recommendation based on bridging movie feature and user interest

Cited by 42 publications

References 19 publications

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

Apple harvesting robot under information technology: A review

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

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