scholarly journals Evolutionary Social Poisson Factorizationfor Temporal Recommendation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
ChunYan Yin ◽  
YongHeng Chen ◽  
Wanli Zuo
Keyword(s):  
Recommendation Systems ◽  
Predictive Performance ◽  
User Preferences ◽  
Social Model ◽  
User Preference ◽  
Latent Factors ◽  
Factorization Model ◽  
Real World Datasets ◽  
Item Attributes ◽  
The Impact

AbstractPreference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF$$\_$$ _ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF$$\_$$ _ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.

Cross Domain Framework for Implementing Recommendation Systems Based on Context Based Implicit Negative Feedback

2012 ◽  
Vol 3 (1) ◽  
pp. 22-36
Author(s):  
Maitri Jhaveri ◽  
Jyoti Pareek
Keyword(s):  
Negative Feedback ◽  
Information Overload ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
P2p Networks ◽  
Cross Domain ◽  
Collaborative Environment ◽  
Available Information ◽  
Novel Model

The last decade met a remarkable proliferation of P2P networks, PDMS, semantic web, communitarian websites, electronic stores, etc. resulting in an overload of available information. One of the solutions to this information overload problem is using efficient tools such as the recommender system which is a personalization system that helps users to find items of interest based on their preferences. Several such recommendation engines do exist under different domains. However these recommendation systems are not very effective due to several issues like lack of data, changing data, changing user preferences, and unpredictable items. This paper proposes a novel model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. This model is based on studying implicit negative feedback from users in cross domain collaborative environment to identify user preferences effectively. The authors have also identified a list of parameters for this study.

User Preference for Instructional Formats: An Evaluation of Performance

1983 ◽  
Vol 27 (6) ◽  
pp. 441-444
Author(s):  
Craig J. Petrun ◽  
Suzanne Henry ◽  
Marian MacDonald ◽  
Robert Torrey ◽  
Eric Anderson
Keyword(s):  
User Preferences ◽  
User Preference ◽  
Preference Data ◽  
Operator Performance ◽  
Evaluation Of Performance ◽  
Performance Differences ◽  
Significant Performance ◽  
And Performance ◽  
Instructional Formats ◽  
The Impact

Three studies were performed to determine the effect of different formats on user preferences and performance. In Studies 1 and 2, operators were presented instructional formats which differed on the following variables: 1) the use of graphic illustrations, 2) the amount and use of color, 3) the type of blocking used to separate the information on each page. The results of both studies indicated that operator preferences were significantly affected by the use of graphics, random blocking, and amount of color. Study 3 examined the impact of preferred and non-preferred formats on operator performance and their preferences after using a given format. The results showed that there were no significant performance differences between the formats. The preference data, however, demonstrated that formats containing color, random blocking, and graphics were still the most preferred variables.

scholarly journals Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network

2019 ◽  
Author(s):  
Zheng Liu ◽  
Yu Xing ◽  
Fangzhao Wu ◽  
Mingxiao An ◽  
Xing Xie
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Recommendation Systems ◽  
User Preference ◽  
High Fidelity ◽  
Learning Techniques ◽  
Entire History ◽  
Real World Datasets ◽  
User History ◽  
Candidate Item

Deep learning techniques have been widely applied to modern recommendation systems, bringing in flexible and effective ways of user representation. Conventionally, user representations are generated purely in the offline stage. Without referencing to the specific candidate item for recommendation, it is difficult to fully capture user preference from the perspective of interest. More recent algorithms tend to generate user representation at runtime, where user's historical behaviors are attentively summarized w.r.t. the presented candidate item. In spite of the improved efficacy, it is too expensive for many real-world scenarios because of the repetitive access to user's entire history. In this work, a novel user representation framework, Hi-Fi Ark, is proposed. With Hi-Fi Ark, user history is summarized into highly compact and complementary vectors in the offline stage, known as archives. Meanwhile, user preference towards a specific candidate item can be precisely captured via the attentive aggregation of such archives. As a result, both deployment feasibility and superior recommendation efficacy are achieved by Hi-Fi Ark. The effectiveness of Hi-Fi Ark is empirically validated on three real-world datasets, where remarkable and consistent improvements are made over a variety of well-recognized baseline methods.

scholarly journals A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhao Huang ◽  
Pavel Stakhiyevich
Keyword(s):  
Hybrid Approach ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
User Profiles ◽  
Group Recommendation ◽  
Group Recommendations ◽  
Time Awareness ◽  
Hybrid Recommendation ◽  
Time Aware

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

scholarly journals An Adaptive Heterogeneous Online Learning Ensemble Classifier for Nonstationary Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tinofirei Museba ◽  
Fulufhelo Nelwamondo ◽  
Khmaies Ouahada
Keyword(s):  
Concept Drift ◽  
Predictive Performance ◽  
Dynamic Environments ◽  
Ensemble Classifiers ◽  
Data Generation ◽  
Ensemble Selection ◽  
Different Types ◽  
Real World Datasets ◽  
The Impact ◽  
Dynamic Ensemble Selection

In recent years, the prevalence of technological advances has led to an enormous and ever-increasing amount of data that are now commonly available in a streaming fashion. In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. Given the increasingly common applications whose data generation mechanisms are susceptible to change, the need for effective and efficient algorithms for learning from and adapting to evolving or drifting environments can hardly be overstated. In dynamic environments associated with concept drift, learning models are frequently updated to adapt to changes in the underlying probability distribution of the data. A lot of work in the area of learning in nonstationary environments focuses on updating the learning predictive model to optimize recovery from concept drift and convergence to new concepts by adjusting parameters and discarding poorly performing models while little effort has been dedicated to investigate what type of learning model is suitable at any given time for different types of concept drift. In this paper, we investigate the impact of heterogeneous online ensemble learning based on online model selection for predictive modeling in dynamic environments. We propose a novel heterogeneous ensemble approach based on online dynamic ensemble selection that accurately interchanges between different types of base models in an ensemble to enhance its predictive performance in nonstationary environments. The approach is known as Heterogeneous Dynamic Ensemble Selection based on Accuracy and Diversity (HDES-AD) and makes use of models generated by different base learners to increase diversity to circumvent problems associated with existing dynamic ensemble classifiers that may experience loss of diversity due to the exclusion of base learners generated by different base algorithms. The algorithm is evaluated on artificial and real-world datasets with well-known online homogeneous online ensemble approaches such as DDD, AFWE, and OAUE. The results show that HDES-AD performed significantly better than the other three homogeneous online ensemble approaches in nonstationary environments.

scholarly journals A Personalized System for Conversational Recommendations

2004 ◽  
Vol 21 ◽  
pp. 393-428 ◽  
Author(s):  
C. A. Thompson ◽  
M. H. Goker ◽  
P. Langley
Keyword(s):  
Recommendation Systems ◽  
User Model ◽  
User Preferences ◽  
Item Selection ◽  
Personalized Recommendation ◽  
Control Group ◽  
Dialogue Systems ◽  
Personalized System ◽  
Item Attributes

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

Understanding Rising Income Inequality and Stagnating Ordinary Living Standards in Germany

2018 ◽  
Author(s):  
Gerhard Bosch ◽  
Thorsten Kalina
Keyword(s):  
Income Inequality ◽  
Labour Market ◽  
Economic Crisis ◽  
Household Income ◽  
Wage Bargaining ◽  
Living Standards ◽  
Social Model ◽  
Real Wages ◽  
The Impact ◽  
Over Time

This chapter describes how inequality and real incomes have evolved in Germany through the period from the 1980s, through reunification, up to the economic Crisis and its aftermath. It brings out how reunification was associated with a prolonged stagnation in real wages. It emphasizes how the distinctive German structures for wage bargaining were eroded over time, and the labour market and tax/transfer reforms of the late 1990s-early/mid-2000s led to increasing dualization in the labour market. The consequence was a marked increase in household income inequality, which went together with wage stagnation for much of the 1990s and subsequently. Coordination between government, employers, and unions still sufficed to avoid the impact the economic Crisis had on unemployment elsewhere, but the German social model has been altered fundamentally over the period

scholarly journals Analysis of EV Cost-Based Charging Load Profiles

Proceedings ◽  
2020 ◽  
Vol 65 (1) ◽  
pp. 2
Author(s):  
Elisavet Koutsi ◽  
Sotirios Deligiannis ◽  
Georgia Athanasiadou ◽  
Dimitra Zarbouti ◽  
George Tsoulos
Keyword(s):  
Electric Vehicles ◽  
User Preferences ◽  
Reference Scenario ◽  
Electricity Prices ◽  
Horizon 2020 ◽  
Vehicle To Grid ◽  
Electrical Grid ◽  
Incentive Strategies ◽  
Grid Load ◽  
The Impact

During the last few decades, electric vehicles (EVs) have emerged as a promising sustainable alternative to traditional fuel cars. The work presented here is carried out in the context of the Horizon 2020 project MERLON and targets the impact of EVs on electrical grid load profiles, while considering both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation modes. Three different charging policies are considered: the uncontrolled charging, which acts as a reference scenario, and two strategies that fall under the umbrella of individual charging policies based on price incentive strategies. Electricity prices along with the EV user preferences are taken into account for both charging (G2V) and discharging (V2G) operations, allowing for more realistic scenarios to be considered.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

scholarly journals Evaluating the effect of compressing algorithms for trajectory similarity and classification problems

2021 ◽  
Author(s):  
Antonios Makris ◽  
Camila Leite da Silva ◽  
Vania Bogorny ◽  
Luis Otavio Alvares ◽  
Jose Antonio Macedo ◽  
...  
Keyword(s):  
Trajectory Analysis ◽  
Similarity Measures ◽  
Classification Problems ◽  
Trajectory Data ◽  
Compression Algorithms ◽  
Time Ratio ◽  
Ratio Speed ◽  
Trajectory Similarity ◽  
Real World Datasets ◽  
The Impact

AbstractDuring the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we focus on data compression techniques with the intention to minimize the size of trajectory data, while, at the same time, minimizing the impact on the trajectory analysis methods. To this extent, we evaluate five lossy compression algorithms: Douglas-Peucker (DP), Time Ratio (TR), Speed Based (SP), Time Ratio Speed Based (TR_SP) and Speed Based Time Ratio (SP_TR). The comparison is performed using four distinct real world datasets against six different dynamically assigned thresholds. The effectiveness of the compression is evaluated using classification techniques and similarity measures. The results showed that there is a trade-off between the compression rate and the achieved quality. The is no “best algorithm” for every case and the choice of the proper compression algorithm is an application-dependent process.

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