Online movie recommendations are generated by grouping users with similar interests into the same category. The recommendation list in streaming hubs like LikewiseTV is also based on user reviews. These reviews contain more detailed information than browsing history and can express user sentiment. Therefore, these reviews can be a key component in determining whether a movie is recommended or not. Recommendation systems have been around for a while and have helped make people’s lives easier. Recommender systems are algorithms that try to propose to consumers relevant objects (items being books to read, goods to buy, music to listen to, or in our instance, movies to watch).

Sentiment Analysis

We can improve the preliminary movie recommendation list by analyzing the sentiment of movie reviews. For this purpose, we can use an implicit sentiment analysis technique that involves building a lexicon based on the field of movies. For example, if a movie is rated very positively, its reviewer will be more likely to recommend it to other users.

This study applies new feature extraction techniques and hybrid deep-learning techniques for sentiment analysis. We also exploit the strengths of BERT and incorporate sentiments into recommender systems. This approach has many potential applications, such as improving the performance of recommendation systems.

Unimodal Recommendation Schemes

In recent years, movie recommendation systems have evolved remarkably, with new, complex ways of building recommendation systems. The emergence of these new technologies has made earlier models obsolete. Today, vector-based search algorithms are sweeping the industry.

Despite the various types of recommendation systems, a common characteristic of all these systems is that they support multimodality. For example, an image may have information that cannot be conveyed in a text, affecting a user’s decision to view or skip a particular movie. A movie recommendation system that includes image features also improves the ability of users to understand a movie’s nuances and characteristics.

Content-Based Filtering

Rather than relying on generalized suggestions, content-based filtering for movie recommendations draws upon data from several users. This is done by comparing user preferences, such as genre and popularity, with data about a film’s production house, director, and lead actors. The system finds movies similar to the selected title based on these factors.

Content-based recommenders generate recommendations based on similar users’ viewing and watching habits. The underlying concept is that a user prefers certain movies based on their past preferences. This approach can also be applied to collaborative filters, such as movie recommendation systems. Content-based recommenders can learn about user preferences by assigning similarity scores to items in their database. Different techniques can be used for this purpose, including cosine similarity, Pearson similarity, Dot Product, and Euclidian distance. These methods work by assigning similarity scores to data points and using the similarity scores to determine the best recommendation. The algorithm can then compare the items that are similar based on these scores.

Collaborative Filtering Approach

This strategy is predicated on the notion that the user evaluates, and the system will suggest other films that the user has not seen but that other users like our test user have watched and enjoyed. The User-to-User Collaborative Filtering Approach is the name given to this kind of collaborative filtering strategy since we identify users who are similar to our users.

We take into account the movies that both users have watched and their ratings of those films to determine whether or not the two users are comparable. Thus, based on similar user ratings, we may anticipate the ratings a user who hasn’t seen the movie yet will give it by examining factors in common.

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