Dominance relationship analysis with budget constraints

Ge, Shen; U, Leong Hou; Mamoulis, Nikos; Cheung, David W.

doi:10.1007/s10115-013-0694-y

Cited by 14 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in our study, we determine the probabilities based on dominating regions of evaluated data points that are very complex in high dimensions. Similar to previous works explained earlier, [11] and [6] also use given customer preferences to determine the upgrading product and it is also different from our proposed approach.…”

Section: Related Workmentioning

confidence: 98%

“…In 2013, the other study of DRA is [11] that takes consideration of budget constraint because every vendor do not have an unlimited budget to upgrade their products. The budget constraint is represented by a plane that is constructed by a constraint function.…”

Section: Related Workmentioning

confidence: 99%

“…The work of upgrading products recommendation system attracts many researchers such as [6], [7], [8], [9], [10], and [11]. In general, those works assume that the customer preferences are fixed and the upgrading or choosing the profitable products are based on fixed customer preferences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Upgrading Products based on Existing Dominant Competitors

Wijayanto

Thamrin²,

Chen

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

In the Industry 4.0 era, manufacturers compete to produce better products that are expected to satisfy a larger number of customers. We propose a recommendation system for upgrading products considering user preferences. This approach is based on the dominating regions of dominant competitors. The dominating region represents the estimation of the number of potential customers. However, examining overlapped dominating regions for a high dimensional space is NP-hard. We propose a novel method named TDRDFS which constructs a Dominant Graph of Intersection skyline points (DGI) for modeling the dominating regions. Our experiments show that TDRDFS significantly reduces computation. Based on our approach, product vendors are able to determine the strategy of upgrading products easily.

show abstract

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Upgrading Products based on Existing Dominant Competitors

Wijayanto

Thamrin²,

Chen

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Ge et. al [18] introduce a new dominance function to evaluate the goodness of a product and aim to find the best new product position under the given budget. Wan et.…”

Section: B Dominance Based Influence Queriesmentioning

confidence: 99%

“…, [18] [6] takes sampling approach O(n r (ρn c ) d/2 +1 ) time to compute a partition where n r is the number of sampling rounds.…”

Section: Sampling Based Solutionmentioning

confidence: 99%

Influence based cost optimization on user preference

Yang

Zhang

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

The popularity of e-business and preference learning techniques have contributed a huge amount of product and user preference data. Analyzing the influence of an existing or new product among the users is critical to unlock the great scientific and social-economic value of these data. In this paper, we advocate the problem of influence-based cost optimization for the user preference and product data, which is fundamental in many real applications such as marketing and advertising. Generally, we aim to find a cost optimal position for a new product such that it can attract at least k or a particular percentage of users for the given user preference functions and competitors' products. Although we show the solution space of our problem can be reduced to a finite number of possible positions (points) by utilizing the classical k-level computation techniques, the computation cost is still very expensive due to the nature of the high combinatorial complexity of the k-level problem. To alleviate this issue, we develop efficient pruning and query processing techniques to significantly improve the performance. In particular, our traverse-based 2-dimensional algorithm is very efficient with time complexity O(n) where n is the number of user preference functions. For general multi-dimensional spaces, we develop space partition based algorithm to significantly improve the performance by utilizing cost-based, influence-based and local dominance based pruning techniques. Then, we show that the performance of the partition based algorithm can be further enhanced by utilizing sampling approach, where the problem can be reduced to the classical half-space intersection problem. We demonstrate the efficiency of our techniques with extensive experiments over real and synthetic datasets.Suppose a company wants to design a new type of laptop (i.e., a point in the 2-dimensional space) to attract at least 4 out of the 5 users; that is, occupying at least 80% of the market share in this example. Following the convention [6], we use a preference line H i to represent a user linear preference 978-1-5090-2020-1/16/$31.00

show abstract

Cost optimization based on influence and user preference

Yang

Zhang

et al. 2019

Knowl Inf Syst

View full text Add to dashboard Cite

Dominance relationship analysis with budget constraints

Cited by 14 publications

References 19 publications

Upgrading Products based on Existing Dominant Competitors

Upgrading Products based on Existing Dominant Competitors

Influence based cost optimization on user preference

Cost optimization based on influence and user preference

Contact Info

Product

Resources

About