|
ROLE
 Author only
BOOKMARK & SHARE
|
|
1
April 2017
WWW '17: Proceedings of the 26th International Conference on World Wide Web
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 14, Downloads (12 Months): 48, Downloads (Overall): 48
Full text available:
 PDF
In order to match shoppers with desired products and provide personalized promotions, whether in online or offline shopping worlds, it is critical to model both consumer preferences and price sensitivities simultaneously. Personalized preferences have been thoroughly studied in the field of recommender systems, though price (and price sensitivity) has received ...
Keywords:
matrix factorization, price elasticity, consumer behavior, recommender system
2
February 2017
WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 34, Downloads (12 Months): 77, Downloads (Overall): 77
Full text available:
PDF
Bartering is a timeless practice that is becoming increasingly popular on the Web. Recommending trades for an online bartering platform shares many similarities with traditional approaches to recommendation, in particular the need to model the preferences of users and the properties of the items they consume. However, there are several ...
Keywords:
collaborative filtering, reciprocity, barter, temporal dynamics, exchange, social dynamics, matrix factorization, swap
3
September 2016
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 18, Downloads (12 Months): 263, Downloads (Overall): 263
Full text available:
PDF
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as ...
Keywords:
Markov chains, artistic preferences, recommender systems
4
July 2016
IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity ...
5
April 2016
WWW '16: Proceedings of the 25th International Conference on World Wide Web
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 32, Downloads (12 Months): 199, Downloads (Overall): 263
Full text available:
PDF
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over ...
Keywords:
personalized ranking, visual dimensions, fashion evolution, recommender systems
6
April 2016
WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 10, Downloads (12 Months): 153, Downloads (Overall): 183
Full text available:
PDF
To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are ...
Keywords:
visualization, recommendation, fashion trends
7
April 2016
WWW '16: Proceedings of the 25th International Conference on World Wide Web
Publisher: International World Wide Web Conferences Steering Committee
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 11, Downloads (12 Months): 110, Downloads (Overall): 139
Full text available:
PDF
Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in the overhead compartment of a 747?' or `will I like this album if I liked ...
Keywords:
bilinear models, reviews, question answering, relevance ranking, text modeling
8
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Modern recommender systems model people and items by discovering or 'teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender ...
9
December 2015
ICCV '15: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE Computer Society
With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like 'What outfit goes well with this pair of shoes?' To answer these types of questions, one has to ...
10
October 2015
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 8, Downloads (12 Months): 100, Downloads (Overall): 300
Full text available:
PDF
Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner ...
Keywords:
collaborative filtering, one-class recommendation, personalized feature projection
11
September 2015
RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 13, Downloads (12 Months): 131, Downloads (Overall): 301
Full text available:
PDF
In implicit feedback datasets, non-interaction of a user with an item does not necessarily indicate that an item is irrelevant for the user. Thus, evaluation measures computed on the observed feedback may not accurately reflect performance on the complete data. In this paper, we discuss a missing data model for ...
Keywords:
evaluation, ranking, recommender systems
12
August 2015
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 31
Downloads (6 Weeks): 20, Downloads (12 Months): 270, Downloads (Overall): 671
Full text available:
PDF
To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. ...
Keywords:
link prediction, recommender systems, topic models, substitutes and complements
13
August 2015
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 25
Downloads (6 Weeks): 31, Downloads (12 Months): 347, Downloads (Overall): 898
Full text available:
PDF
Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of ...
Keywords:
metric learning, visual features, recommender systems
14
November 2014
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 23
Downloads (6 Weeks): 20, Downloads (12 Months): 129, Downloads (Overall): 466
Full text available:
PDF
Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking . Although ...
Keywords:
recommender systems, social networks, personalized ranking
15
April 2014
WWW '14: Proceedings of the 23rd international conference on World wide web
Publisher: ACM
Bibliometrics:
Citation Count: 12
Downloads (6 Weeks): 7, Downloads (12 Months): 85, Downloads (Overall): 458
Full text available:
PDF
Event sequences, such as patients' medical histories or users' sequences of product reviews, trace how individuals progress over time. Identifying common patterns, or progression stages, in such event sequences is a challenging task because not every individual follows the same evolutionary pattern, stages may have very different lengths, and individuals ...
Keywords:
time series, user modeling, event sequences
16
February 2014
WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 14, Downloads (12 Months): 53, Downloads (Overall): 318
Full text available:
PDF
Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying ...
Keywords:
network communities, overlapping community detection, 2-mode communities
17
February 2014
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue: Volume 8 Issue 1, February 2014
Publisher: ACM
Bibliometrics:
Citation Count: 20
Downloads (6 Weeks): 10, Downloads (12 Months): 193, Downloads (Overall): 1,008
Full text available:
PDF
People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g., “circles” on Google+, and “lists” on Facebook and Twitter). However, circles are laborious to construct and must ...
Keywords:
ego networks, Community detection, machine learning, social circles
18
October 2013
RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 115
Downloads (6 Weeks): 62, Downloads (12 Months): 789, Downloads (Overall): 2,530
Full text available:
PDF
In order to recommend products to users we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will ...
Keywords:
topic models, recommender systems
19
May 2013
WWW '13: Proceedings of the 22nd international conference on World Wide Web
Publisher: ACM
Bibliometrics:
Citation Count: 33
Downloads (6 Weeks): 11, Downloads (12 Months): 142, Downloads (Overall): 728
Full text available:
PDF
Recommending products to consumers means not only understanding their tastes , but also understanding their level of experience . For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy ...
Keywords:
recommender systems, user modeling, expertise
20
December 2012
ICDM '12: Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Publisher: IEEE Computer Society
Most online reviews consist of plain-text feedback together with a single numeric score. However, understanding the multiple `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audio book presumably depends on aspects such as the story and ...
Keywords:
machine learning, segmentation, summarization, sentiment analysis
|
|
