Content-based Filtering Algorithm Research Articles

The Implementation of Recommender Systems for Mental Health Recovery Narratives: Evaluation of Use and Performance.

Recommender systems help narrow down a large range of items to a smaller, personalized set. NarraGive is a first-in-field hybrid recommender system for mental health recovery narratives, recommending narratives based on their content and narrator characteristics (using content-based filtering) and on narratives beneficially impacting other similar users (using collaborative filtering). NarraGive is integrated into the Narrative Experiences Online (NEON) intervention, a web application providing access to the NEON Collection of recovery narratives. This study aims to analyze the 3 recommender system algorithms used in NarraGive to inform future interventions using recommender systems for lived experience narratives. Using a recently published framework for evaluating recommender systems to structure the analysis, we compared the content-based filtering algorithm and collaborative filtering algorithms by evaluating the accuracy (how close the predicted ratings are to the true ratings), precision (the proportion of the recommended narratives that are relevant), diversity (how diverse the recommended narratives are), coverage (the proportion of all available narratives that can be recommended), and unfairness (whether the algorithms produce less accurate predictions for disadvantaged participants) across gender and ethnicity. We used data from all participants in 2 parallel-group, waitlist control clinical trials of the NEON intervention (NEON trial: N=739; NEON for other [eg, nonpsychosis] mental health problems [NEON-O] trial: N=1023). Both trials included people with self-reported mental health problems who had and had not used statutory mental health services. In addition, NEON trial participants had experienced self-reported psychosis in the previous 5 years. Our evaluation used a database of Likert-scale narrative ratings provided by trial participants in response to validated narrative feedback questions. Participants from the NEON and NEON-O trials provided 2288 and 1896 narrative ratings, respectively. Each rated narrative had a median of 3 ratings and 2 ratings, respectively. For the NEON trial, the content-based filtering algorithm performed better for coverage; the collaborative filtering algorithms performed better for accuracy, diversity, and unfairness across both gender and ethnicity; and neither algorithm performed better for precision. For the NEON-O trial, the content-based filtering algorithm did not perform better on any metric; the collaborative filtering algorithms performed better on accuracy and unfairness across both gender and ethnicity; and neither algorithm performed better for precision, diversity, or coverage. Clinical population may be associated with recommender system performance. Recommender systems are susceptible to a wide range of undesirable biases. Approaches to mitigating these include providing enough initial data for the recommender system (to prevent overfitting), ensuring that items can be accessed outside the recommender system (to prevent a feedback loop between accessed items and recommended items), and encouraging participants to provide feedback on every narrative they interact with (to prevent participants from only providing feedback when they have strong opinions).

Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations

With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.

Content-based filtering algorithm in social media

Content-based filtering is a recommendation algorithm that analyzes user activity and profile data to provide personalized recommendations for content that matches a user's interests and preferences. This algorithm is widely used by social media platforms, such as Facebook and Twitter, to increase user engagement and satisfaction. The methodology of content-based filtering involves creating a user profile based on user activity and recommending content that matches the user's interests. The algorithm continually updates and personalizes the recommendations based on user feedback, and incorporates strategies to promote diversity and serendipity in the recommendations. While content-based filtering has some limitations, it remains a powerful tool in the arsenal of social media platforms, offering efficient content discovery and personalized user experiences at scale.

The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.

A Comprehensive Survey Using Novel Content-Based Filtering Technique to Improve Effectiveness on Social Media Datasets for Group Recommendation System Based on External Social Trust Networks

Aim:To build a Movie Recommendation system for Netflix to improve the accuracy percentage using Content-Based Filtering technique to analyze the prediction rate of movies. Materials and Methods: Two different algorithms are used Content-Based filtering algorithm and External Social Trust Networks to achieve maximum accuracy with a content based filtering with a sample size = 6 and external social trust network with a sample size= 6 was iterated 10 times for accurate outcome. Pre test is calculated by using G-Power tool value of 80% and confidence interval is 95% mean and standard deviation. Result and Discussions: These results proved that Recommendation System for Netflix using Novel Content-Based filtering algorithm gives better accuracy (0.94) in comparison with External Social Trust Networks(0.76). Conclusion: Content-Based Filtering algorithm helps to build a recommendation system gives effective results with the better accuracy rate of percentage.

Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System

This is the era of I-way. The development of high-speed computing and huge storage devices change the working culture of human. It affects the traditional business processes and shifted towards online business. It creates huge problems like overload and irrelevant information which are the causes of confusion both customers as well as enterprise. Recommendation system solves these problems. Design and development of efficient system is one of the key areas of the recent researchers. Collaborative filtering (CF) and content-based filtering algorithms are widely used in the implementation of such system. Collaborative used user’s features while content-based used item’s features. Most of the CFs are rating or review based processed homogeneous information. In this paper we proposed knowledge-based collaborative filtering algorithm for large data set that uses various activities done by users during interaction of item through E-commerce web site like clicks, select and purchase. The performance of the system is compared with the base models using real time Amazon E-commerce dataset using precession, recall and NDCG evaluation parameters in various combinations of activities performed by users on items.

A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems

In the modern age and many prestigious applications use the recommendation method to play an important role. The system of recommendations collected apps, built a global village and provided enough information for development. This paper presents an overview of the approaches and techniques produced in the recommendation framework for collaborative filtering. Collaborative filtering, material and hybrid methods were the method of recommendation. In producing personalised recommendation the technique of collaborative filtering is particularly effective. There have been several algorithms over ten years of study, but no distinctions have been made between the various strategies. Indeed, there is not yet a widely agreed way to test a collaborative filtering algorithm. In this work we compare various literature techniques and review each one’s characteristics to emphasise their key strengths and weaknesses.

A Hybrid Recommendation System for Marine Science Observation Data Based on Content and Literature Filtering.

With the development of ocean exploration technology and the rapid growth in the amount of marine science observation data, people are faced with a great challenge to identify valuable data from the massive ocean observation data. A recommendation system is an effective method to improve retrieval capabilities to help users obtain valuable data. The two most popular recommendation algorithms are collaborative filtering algorithms and content-based filtering algorithms, which may not work well for marine science observation data given the complexity of data attributes and lack of user information. In this study, an approach was proposed based on data similarity and data correlation. Data similarity was calculated by analyzing the subject, source, spatial, and temporal attributes to obtain the recommendation list. Then, data correlation was calculated based on the literature on marine science data and ranking of the recommendation list to obtain the re-rank recommendation list. The approach was tested by simulated datasets collected from multiple marine data sharing websites, and the result suggested that the proposed method exhibits better effectiveness.

A fuzzy hybrid approach to enhance diversity in top-N recommendations

In recent researches, recommender systems performance was restricted in their ability to predict unseen items by users and recommending them those with the highest predicted ratings. To make good predictions it is not sufficient to judge the performance, because a good recommender should offer useful and diverse items that fit to different interest choices and tastes of users. Unfortunately, the lack of diversity and the stability in recommender systems over the user's profile dynamicity become a major challenge. In this paper, we propose a fuzzy hybrid diversified recommendation system able to generate multi-taste recommendations depending on the user's profile variation. Due to the fuzziness and uncertainty in user's profile, the system allows users belonging to different clusters using a fuzzy-based collaborative filtering combined with a content-based filtering algorithm. To identify the user's neighbourhood, a novel similarity measure is proposed. To increase diversity in recommended lists, subsets from Top-N recommended lists in similar clusters, are selected according to users membership degrees. Several experiments are conducted on Movilens dataset to prove the proposal's effectiveness.

Symbiotic filtering for spam email detection

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall performance of spam detection. SF is an hybrid approach combining some features from both Collaborative (CF) and Content-Based Filtering (CBF). It allows for the use of social networks to personalize and tailor the set of filters that serve as input to the filtering. A comparison is performed against the commonly used Naive Bayes CBF algorithm. Several experiments were held with the well-known Enron data, under both fixed and incremental symbiotic groups. We show that our system is competitive in performance and is robust against both dictionary and focused contamination attacks. Moreover, it can be implemented and deployed with few effort and low communication costs, while assuring privacy.