Papers by Ahmad Abdel-Hafez
Lecture Notes in Computer Science, 2015
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Proceedings of the 2014 Recommender Systems Challenge on - RecSysChallenge '14, 2014
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Many websites presently provide the facility for users to rate items quality based on user opinio... more Many websites presently provide the facility for users to rate items quality based on user opinion. These ratings are used later to produce item reputation scores. The majority of websites apply the mean method to aggregate user ratings. This method is very simple and is not considered as an accurate aggregator. Many methods have been proposed to make aggregators produce more accurate reputation scores. In the majority of proposed methods the authors use extra information about the rating providers or about the context (e.g. time) in which the rating was given. However, this information is not available all the time. In such cases these methods produce reputation scores using the mean method or other alternative simple methods. In this paper, we propose a novel reputation model that generates more accurate item reputation scores based on collected ratings only. Our proposed model embeds statistical data, previously disregarded, of a given rating dataset in order to enhance the accuracy of the generated reputation scores. In more detail, we use the Beta distribution to produce weights for ratings and aggregate ratings using the weighted mean method. Experiments show that the proposed model exhibits performance superior to that of current state-of-the-art models.
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With the extensive use of rating systems in the web, and their significance in decision making pr... more With the extensive use of rating systems in the web, and their significance in decision making process by users, the need for more accurate aggregation methods has emerged. The Naïve aggregation method, using the simple mean, is not adequate anymore in providing accurate reputation scores for items, hence, several researches where conducted in order to provide more accurate alternative aggregation methods. Most of the current reputation models do not consider the distribution of ratings across the different possible ratings values. In this paper, we propose a novel reputation model, which generates more accurate reputation scores for items by deploying the normal distribution over ratings. Experiments show promising results for our proposed model over state-of-the-art ones on sparse and dense datasets.
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Multidimensional data are getting increasing attention from researchers for creating better recom... more Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically, and propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.
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Recommender systems provide personalized advice for customers online based on their own preferen... more Recommender systems provide personalized advice for customers online based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems use users’ ratings to generate their output. In this paper, we propose to combine reputation models with recommender systems to enhance the accuracy of recommendations. The main contributions include two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively, and a personalized reputation method to generate item reputations based on users’ interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.
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Reputation systems are employed to provide users with advice on the quality of items on the Web, ... more Reputation systems are employed to provide users with advice on the quality of items on the Web, based on the aggregated value of user-based ratings. Recommender systems are used online to suggest items to users according to the users, expressed preferences. Yet, recommender systems will endorse an item regardless of its reputation value. In this paper, we report the incorporation of reputation models into recommender systems to enhance the accuracy of recommendations. The proposed method separates the implementation of recommender and reputation systems for generality. Our experiment showed that the proposed method could enhance the accuracy of existing recommender systems.
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Twitter is a very popular social network website that allows users to publish short posts called ... more Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A sub-problem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.
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Rating systems are used by many websites, which allow customers to rate available items according... more Rating systems are used by many websites, which allow customers to rate available items according to their own experience. Subsequently, reputation models are used to aggregate available ratings in order to generate reputation scores for items. A problem with current reputation models is that they provide solutions to enhance accuracy of sparse datasets not thinking of their models performance over dense datasets. In this paper, we propose a novel reputation model to generate more accurate reputation scores for items using any dataset; whether it is dense or sparse. Our proposed model is described as a weighted average method, where the weights are generated using the normal distribution. Experiments show promising results for the proposed model over state-of-the-art ones on sparse and dense datasets.
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With the widespread of social media websites in the internet, and the huge number of users partic... more With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users’ profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.
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In recent years, the Web 2.0 has provided considerable facilities for people to create, share and... more In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.
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Many websites oer the opportunity for customers to rate
items and then use customers' ratings to... more Many websites oer the opportunity for customers to rate
items and then use customers' ratings to generate items reputation,
which can be used later by other users for decision
making purposes. The aggregated value of the ratings per
item represents the reputation of this item. The accuracy of
the reputation scores is important as it is used to rank items.
Most of the aggregation methods didn't consider the frequency
of distinct ratings and they didn't test how accurate
their reputation scores over different datasets with different
sparsity. In this work we propose a new aggregation method
which can be described as a weighted average, where weights
are generated using the normal distribution. The evaluation
result shows that the proposed method outperforms state-of-
the-art methods over different sparsity datasets.
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Different reputation models are used in the web in order to generate reputation values for produc... more Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.
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Computer and Information Science, Jan 1, 2011
The objective of persuasive technology researches is to develop persuasive systems that are able ... more The objective of persuasive technology researches is to develop persuasive systems that are able to change or reshape human behavior. Persuasive technology has quickly found a wide range of applications in many fields of research and development like marketing, health, safety and environment. The key element in designing successful persuasive systems is the improvement of the persuasion process. An important factor that should be included in the persuasion process is the user experience. This paper reviews the current trends of persuasive technology and shows some example of the available persuasive systems. The contribution of this paper is proposing a new and promising research direction for building persuasive systems that take the user feedback as a key element in the persuasion process. Some of the systems that follow this approach have been proposed and illustrated.
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Product rating systems are very popular on the web, and users are increasingly depending on the o... more Product rating systems are very popular on the web, and users are increasingly depending on the overall product ratings provided by websites to make purchase decisions or to compare various products. Currently most of these systems directly depend on users' ratings and aggregate the ratings using simple aggregating methods such as mean or median [1]. In fact, many websites also allow users to express their opinions in the form of textual product reviews.
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Persuasive technology is a recent research field that combines the computing and human psychology... more Persuasive technology is a recent research field that combines the computing and human psychology aspects. The target is to develop persuasive systems that are able to change or reshape human behavior. Persuasive technology has quickly found a wide range of applications in many fields of research and development like marketing, health, safety and environment. Persuasive systems may also be used in other domains like religion, politics, diplomacy, military training, management, and education. Improving the persuasion process is a key element in the success of persuasive systems. The user experience is an important factor that should be included in the persuasion process. This paper reviews the current trends of persuasive technology and shows some example of the available persuasive systems. It then contributes by proposing a new and promising research direction for designing persuasive systems that take the user feedback as a main element in the persuasion process. Some of the systems that follow this approach have been proposed and illustrated.
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Thesis Chapters by Ahmad Abdel-Hafez
This thesis introduced two novel reputation models to generate accurate item reputation scores us... more This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.
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Papers by Ahmad Abdel-Hafez
items and then use customers' ratings to generate items reputation,
which can be used later by other users for decision
making purposes. The aggregated value of the ratings per
item represents the reputation of this item. The accuracy of
the reputation scores is important as it is used to rank items.
Most of the aggregation methods didn't consider the frequency
of distinct ratings and they didn't test how accurate
their reputation scores over different datasets with different
sparsity. In this work we propose a new aggregation method
which can be described as a weighted average, where weights
are generated using the normal distribution. The evaluation
result shows that the proposed method outperforms state-of-
the-art methods over different sparsity datasets.
Thesis Chapters by Ahmad Abdel-Hafez
items and then use customers' ratings to generate items reputation,
which can be used later by other users for decision
making purposes. The aggregated value of the ratings per
item represents the reputation of this item. The accuracy of
the reputation scores is important as it is used to rank items.
Most of the aggregation methods didn't consider the frequency
of distinct ratings and they didn't test how accurate
their reputation scores over different datasets with different
sparsity. In this work we propose a new aggregation method
which can be described as a weighted average, where weights
are generated using the normal distribution. The evaluation
result shows that the proposed method outperforms state-of-
the-art methods over different sparsity datasets.