Building Personalized and Non Personalized recommendation systems

Khatwani, Sneha; Chandak, M. B.

doi:10.1109/icacdot.2016.7877661

Cited by 16 publications

(7 citation statements)

References 5 publications

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“…Most recommender systems exploit well-known techniques such as content-based and collaborative filtering in order to provide users with personalized suggestions [34], based on the ideas that they will like similar items to those they liked in the past (content-based filtering), or items that are appreciated by other users with similar tastes (collaborative filtering). Non-personalized recommenders [9,10,35], on the contrary, offer the same recommendations to all their users. For example, they select items to recommend based on criteria such as freshness, some editor's choice, item features (e.g., brand, author, product category), product associations or, more often than not, popularity.…”

Section: Non-personalized Recommendersmentioning

confidence: 99%

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When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

et al. 2019

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Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing.

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Section: Non-personalized Recommendersmentioning

confidence: 99%

“…Some recommender systems, however, may be designed (e.g., for practical or juridical reasons) to provide their users with non-personalized recommendations [9,10]. As stated by Ricci et al [11], for example, non-personalized recommendations are typical of magazines and newspapers.…”

Section: Introductionmentioning

confidence: 99%

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

et al. 2019

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“…This strategy takes ratings given by user for any item from the large catalog of item catalog of ratings given by the user. This large catalog is referred as user-item matrix [21]. Figure 3 explains collaborative filtering with an example of recommending movie to the user.…”

Section: Collaborative Filteringmentioning

confidence: 99%

A review of movie recommendation system: Limitations, Survey and Challenges

Sunilkumar

2020

ELCVIA

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Recommendation System is a major area which is very popular and useful for people to take proper automated decisions. It is a method that helps user to find out the information which is beneficial to him/her from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. To overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have surveyed state-of-the-art methods of Content Based Filtering, Collaborative Filtering, Hybrid Approach and Deep Learning Based Methods for movie recommendation. We have also reviewed different similarity measures. Various companies like facebook which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.

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“…from 0 to 5 and is calculated as Score=round (MEAN (ratings)*10) [3]. On the other hand, some websites may display graphs with top-N items based on highest average ratings, while others use percentage of the people who rated an item good or bad.…”

Section: Non-personalized Recommendation Systemmentioning

confidence: 99%

“…It is easy for a user to interpret the results [3]  As only the highly rated items gets displayed, it becomes easy to implement [6]  Data collection is relatively easier in this technique due to limited number of variable required [6] Demerits.  It lacks personal appeal as it does not account for user specific attributes [3]  These systems face challenges in clustered diverse population [3] Product Association Approach.…”

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confidence: 99%

A Recommendation System: Trends And Future

Gupta¹,

Dave²

2019

IJEAT

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Recommendation system plays a key role in ecommerce universe and is used in many applications, websites and more. It has led to synergies between applications, created global village and growth of information. This paper represents the overview of approaches and techniques generated in recommendation systems. Recommendation system is categorized in two classes: Personalized and Non-personalized, which is further divided into various approaches and techniques. This paper discusses each of the methodology in detail highlighting their strengths and weaknesses.

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Building Personalized and Non Personalized recommendation systems

Cited by 16 publications

References 5 publications

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

A review of movie recommendation system: Limitations, Survey and Challenges

A Recommendation System: Trends And Future

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