22,669 research outputs found
Probabilistic group recommendation via information matching
Increasingly, web recommender systems face scenarios where they need to serve suggestions to groups of users; for exam- ple, when families share e-commerce or movie rental web accounts. Research to date in this domain has proposed two approaches: computing recommendations for the group by merging any members’ ratings into a single profile, or com- puting ranked recommendations for each individual that are then merged via a range of heuristics. In doing so, none of the past approaches reason on the preferences that arise in individuals when they are members of a group . In this work, we present a probabilistic framework, based on the notion of information matching, for group recommendation. This model defines group relevance as a combination of the item’s relevance to each user as an individual and as a member of the group; it can then seamlessly incorporate any group rec- ommendation strategy in order to rank items for a set of individuals. We evaluate the model’s efficacy at generating recommendations for both single individuals and groups us- ing the MovieLens and MoviePilot data sets. In both cases, we compare our results with baselines and state-of-the-art collaborative filtering algorithms, and show that the model outperforms all others over a variety of ranking metrics
A Personalized System for Conversational Recommendations
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
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Concepts embody the knowledge of the world and facilitate the cognitive
processes of human beings. Mining concepts from web documents and constructing
the corresponding taxonomy are core research problems in text understanding and
support many downstream tasks such as query analysis, knowledge base
construction, recommendation, and search. However, we argue that most prior
studies extract formal and overly general concepts from Wikipedia or static web
pages, which are not representing the user perspective. In this paper, we
describe our experience of implementing and deploying ConcepT in Tencent QQ
Browser. It discovers user-centered concepts at the right granularity
conforming to user interests, by mining a large amount of user queries and
interactive search click logs. The extracted concepts have the proper
granularity, are consistent with user language styles and are dynamically
updated. We further present our techniques to tag documents with user-centered
concepts and to construct a topic-concept-instance taxonomy, which has helped
to improve search as well as news feeds recommendation in Tencent QQ Browser.
We performed extensive offline evaluation to demonstrate that our approach
could extract concepts of higher quality compared to several other existing
methods. Our system has been deployed in Tencent QQ Browser. Results from
online A/B testing involving a large number of real users suggest that the
Impression Efficiency of feeds users increased by 6.01% after incorporating the
user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling
We evaluate the impact of probabilistically-constructed digital identity data
collected from Sep. to Dec. 2017 (approx.), in the context of
Lookalike-targeted campaigns. The backbone of this study is a large set of
probabilistically-constructed "identities", represented as small bags of
cookies and mobile ad identifiers with associated metadata, that are likely all
owned by the same underlying user. The identity data allows to generate
"identity-based", rather than "identifier-based", user models, giving a fuller
picture of the interests of the users underlying the identifiers. We employ
off-policy techniques to evaluate the potential of identity-powered lookalike
models without incurring the risk of allowing untested models to direct large
amounts of ad spend or the large cost of performing A/B tests. We add to
historical work on off-policy evaluation by noting a significant type of
"finite-sample bias" that occurs for studies combining modestly-sized datasets
and evaluation metrics involving rare events (e.g., conversions). We illustrate
this bias using a simulation study that later informs the handling of inverse
propensity weights in our analyses on real data. We demonstrate significant
lift in identity-powered lookalikes versus an identity-ignorant baseline: on
average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for
identifiers having little data themselves, but that can be inferred to belong
to users with substantial data to aggregate across identifiers. This implies
that identity-powered user modeling is especially important in the context of
identifiers having very short lifespans (i.e., frequently churned cookies). Our
work motivates and informs the use of probabilistically-constructed identities
in marketing. It also deepens the canon of examples in which off-policy
learning has been employed to evaluate the complex systems of the internet
economy.Comment: Accepted by WSDM 201
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