A note on explicit versus implicit information for job recommendation

Reusens, Michael; Lemahieu, Wilfried; Baesens, Bart; Sels, Luc

doi:10.1016/j.dss.2017.04.002

Cited by 40 publications

(27 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The multistakeholder recommendation is an extension of recent efforts to expand the considerations involved in RS evaluation beyond simple accuracy measurements. Prior research has examined specific cases of such concerns in the category of reciprocal recommendations, such as applications in online dating [12], recruitment [13], education [14], advertising [15], and scientific collaboration [16].…”

Section: A Multistakeholder Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

View full text Add to dashboard Cite

Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.

show abstract

Section: A Multistakeholder Recommender Systemsmentioning

confidence: 99%

“…In our work, we calculate the degree of the providers of the recommended items. The provider coverage is defined as follows: (13) where Nprovider is the number of different providers that can be recommended by this system and Ntotal is the total number of providers in the whole system.…”

Section: Many-objective Optimizationmentioning

confidence: 99%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Nguyen et al [22] set the proportion of each activity to its score range to generate a personalized score in a functional form; this is not the best scheme using CF based on the common ground ratings. According to [23], using implicit feedback data resulted in a more diverse and accurate recommendation by providing a wealth of unbiased information compared to using explicit ratings.…”

Section: Related Workmentioning

confidence: 99%

“…It means that the data is not a result from recommendation. For the purpose of performance comparison, we therefore count the number of items purchased based on the assumption that top-N recommended items were presented to users [23,[38][39][40]. At each N, the recall by a user is computed as follows:…”

Section: Performance Measurementmentioning

confidence: 99%

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Lee

Kim

2019

Applied Sciences

View full text Add to dashboard Cite

Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

show abstract

“…A recommender system is a software tool that recommends suitable items to a user or group of users [9]. The recommender systems infer user interests by utilizing various sources of data, such as user profiles, clicks, and feedbacks (rating, and like/dislike) [8,10]. However, in a smart TV environment, such data are neither accurate nor simple to predict or calculate because the smart TV represents a set of users with diverse interests and taste.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

2019

View full text Add to dashboard Cite

The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s).

show abstract

A note on explicit versus implicit information for job recommendation

Cited by 40 publications

References 13 publications

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

Contact Info

Product

Resources

About