Towards a theory model for product search

@inproceedings{Li2011TowardsAT,
  title={Towards a theory model for product search},
  author={Beibei Li and Anindya Ghose and Panagiotis G. Ipeirotis},
  booktitle={WWW},
  year={2011}
}
With the growing pervasiveness of the Internet, online search for products and services is constantly increasing. Most product search engines are based on adaptations of theoretical models devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a… 

Figures and Tables from this paper

A demo search engine for products
TLDR
A theory model for product search based on expected utility theory from economics is proposed, in which a ranking technique is proposed in which the products that generate the highest surplus, after the purchase are ranked.
Enhancing product search by best-selling prediction in e-commerce
TLDR
A new ranking framework for enhancing product search based on dynamic best-selling prediction in E-Commerce is proposed, in which the product items that are not only relevant to the customer's need but with higher probability to be purchased by the customer are ranked.
Increasing temporal diversity with purchase intervals
TLDR
An efficient algorithm is designed to compute the purchase intervals between product pairs from users' purchase history and integrate this information into the marginal utility model and demonstrates that this approach significantly improves the conversion rate and temporal diversity compared to state-of-the-art algorithms.
Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
TLDR
A novel learning framework for EC product search called LETORIF (LEarning To Rank with Implicit Feedback) is presented, which utilizes implicit user feedback signals and jointly model the different stages of the shopping journey to optimize for EC sales revenue.
Leverage Implicit Feedback for Context-aware Product Search
TLDR
This paper leverages clicks within a query session, as implicit feedback, to represent users' hidden intents, which further act as the basis for re-ranking subsequent result pages for the query, and proposes an end-to-end context-aware embedding model which can capture long-term and short-term context dependencies.
A Bayesian Framework for Modeling Price Preference in Product Search
TLDR
Preliminary experiment results with product search log show promise of the proposed Bayesian framework for modeling a user’s price preference, which opens up interesting opportunities for new research in the intersection of machine learning, information retrieval and economics.
Which used product is more sellable? A time-aware approach
TLDR
This paper introduces a novel time-aware metric—“sellability”, which is defined as the time duration for a used item to be traded, to quantify the value of it and proposes a combined Poisson regression and listwise ranking model.
Reducing Buyers' Uncertainty About Taste-Related Product Attributes
TLDR
It is shown that firms must consider the variance, but not the mean, of buyer reviews, to determine the need to invest in reducing consumer uncertainty about taste-related attributes.
User Intent, Behaviour, and Perceived Satisfaction in Product Search
TLDR
Using a series of user interaction features, it is demonstrated that an in-depth understanding of why users search, how they interact with, and perceive the product search results is addressed.
Intelligent Information Systems for Web Product Search
TLDR
This dissertation addresses key aspects of implementing such a system, including hierarchical product classification, entity resolution, ontology population and schema mapping, and lastly, the optimization of faceted user interfaces.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 28 REFERENCES
Show me the money!: deriving the pricing power of product features by mining consumer reviews
TLDR
A novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a Tensor product of feature and evaluation spaces is developed and impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space.
Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets
TLDR
It is suggested that identity-relevant information about reviewers shapes community members' judgment of products and reviews and shows that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce.
The Pure Characteristics Demand Model
In this article, we consider a class of discrete choice models in which consumers care about a finite set of product characteristics. These models have been used extensively in the theoretical
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics
TLDR
This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.
A Hybrid Discrete Choice Model of Differentiated Product Demand with an Application to Personal Computers
TLDR
A new discrete choice model of differentiated product demand that combines the pure characteristics demand model and the random coefficient logit demand model is constructed, which is better identified than the other two models such that coefficients are statistically significant and the willingness to pay for quality improvement is more reasonable.
Measuring Prices and Price Competition Online: Amazon.com and BarnesandNoble.com
Despite the interest in measuring price sensitivity of online consumers, most academic work on Internet commerce is hindered by a lack of data on quantity. In this paper we use publicly available
Stay Elsewhere? Improving Local Search for Hotels Using Econometric Modeling and Image Classification
TLDR
The fact that the overall desirability of the hotel is reected in the price of the rooms is used, using hedonic regressions, an established technique from econometrics, to estimate the weight that consumers place on dierent hotel characteristics.
Opinion Mining using Econometrics: A Case Study on Reputation Systems
TLDR
Econometrics is used to identify the “economic value of text” and assign a “dollar value” to each opinion phrase, measuring sentiment effectively and without the need for manual labeling, and argues that by interpreting opinions using econometricrics, it has the first objective, quantifiable, and contextsensitive evaluation of opinions.
Estimating Discrete-Choice Models of Product Differentiation
This article considers the problem of "supply-and-demand" analysis on a cross section of oligopoly markets with differentiated products. The primary methodology is to assume that demand can be
Optimizing web search using social annotations
TLDR
Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality of a webpage from the web users' perspective.
...
1
2
3
...