Design of Front-End for Recommendation Systems: Towards a Hybrid Architecture

To provide personalized online shopping suggestions, recommendation systems play an increasingly important role in “closing a transaction”. Some leading online movie sales platforms, such as Netflix and Rotten Tomatoes, have exploited content-based recommendation approaches. However, the issue of insufficient information about features in item profiles may lead to less accurate recommendations. In this paper, we propose a recommendation method known as Collective Intelligence Social Tagging (CIST), which combines a content-based recommendation approach with a social tagging function based on crowd-sourcing. We used an online movie sales platform as a use-case of how a CIST approach could increase the accuracy of recommended results and the overall user experience. In order t0 understand the feasibility and satisfaction level for CIST, we conducted fifteen design interviews to first determine user-developer perspectives on CIST, and then collected their overall design input

Recommendation Algorithms Based on Behavioral History.

With the rapid popularity of Internet and mobile Internet, the number of movie entertainment information on the Web is quite huge, and it is increasingly difficult for people to obtain information about movies of interest. In this study, we propose a personalized recommendation strategy based on the sequence of user playing behavior for the personalized recommendation problem of movie websites. The strategy analyzes the user playing video behavior data by Word2vec, a deep neural network word vector model, maps the videos into equal-dimensional feature vectors, calculates the similarity of movies, and uses it as the basis of recommendation to generate a recommendation list to users. In addition, to improve recommendation accuracy, Word2vec parameter settings were considered, and a pre-processing method for history sequences was proposed

Graph-powered recommendation engine in movie recommender system

Usually, people will search on the Internet for movie that they want to watch. However, it is tedious to find and choose movie that matched with their preferences due to a lot of information on the Internet. Therefore, most of the movie portals are adopting recommendation engine to filter and display user’s personalized content. In this paper, MyRecMovie is presented to recommend movies by using graph-powered recommendation engine. MyRecMovie adopts content based (CB) and collaborative filtering (CF) approaches with then further enhance the recommendations with graph-powered recommendation engine to provide movie recommendations to the user

Growth performance, cytokine expression, and immune responses of broiler chickens fed a dietary palm oil and sunflower oil blend supplemented with L-Arginine and varying concentrations of vitamin E

Usually, people will search on the Internet for movie that they want to watch. However, it is tedious to find and choose movie that matched with their preferences due to a lot of information on the Internet. Therefore, most of the movie portals are adopting recommendation engine to filter and display user’s personalized content. In this paper, MyRecMovie is presented to recommend movies by using graph-powered recommendation engine. MyRecMovie adopts content based (CB) and collaborative filtering (CF) approaches with then further enhance the recommendations with graph-powered recommendation engine to provide movie recommendations to the user

The Effectiveness of Personalized Movie Explanations : An Experiment Using Commercial Meta-data

Preprin

Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization

Peer reviewedPostprin

Video Recommendations Based on Visual Features Extracted with Deep Learning

Postponed access: the file will be accessible after 2022-06-01When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

MOBICORS-Movie: A MOBIle COntents Recommender System for Movie

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBIle COntents Recommender System for Movie (MOBICORS-Movie), which is designed to reduce customers’ search efforts in finding desired movies on the mobile Internet. MOBICORS-Movie consists of three agents: CF (Collaborative Filtering), CBIR (Content-Based Information Retrieval) and RF (Relevance Feedback). These agents collaborate each other to support a customer in finding a desired movie by generating personalized recommendations of movies. To verify the performance of MOBICORS-Movie, the simulation-based experiments were conducted. The experiment results show that MOBICORS-Movie significantly reduces the customer’s search effort and can be a realistic solution for movie recommendation in the mobile Internet environment