maximizing product adoption in social networks smriti bhagat, amit goyal, laks lakshmanan (paper...
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![Page 1: Maximizing Product Adoption in Social Networks Smriti Bhagat, Amit Goyal, Laks Lakshmanan (Paper appeared in WSDM 2012)](https://reader035.vdocuments.mx/reader035/viewer/2022062716/56649dbc5503460f94aae18c/html5/thumbnails/1.jpg)
Maximizing Product Adoption in Social Networks
Smriti Bhagat, Amit Goyal, Laks Lakshmanan(Paper appeared in WSDM 2012)
![Page 2: Maximizing Product Adoption in Social Networks Smriti Bhagat, Amit Goyal, Laks Lakshmanan (Paper appeared in WSDM 2012)](https://reader035.vdocuments.mx/reader035/viewer/2022062716/56649dbc5503460f94aae18c/html5/thumbnails/2.jpg)
Viral Marketing Objective: Given a social
network, find a small number of individuals (seed set), who when convinced about a product will influence others by word-of-mouth, leading to a large number of adoptions of the product
Studied as the Influence Maximization Problem§
Node: User in a social network (green – seed set)Edge: Friendship among usersEdge Weight: Influence probability
0.2
0.9
§D. Kempe, J. Kleinberg, and E . Tardos. Maximizing the spread of influence through a social network. In KDD’03. ́�
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Previous Work
Two classical influence propagation models§:• Independent cascades• Linear threshold
- Each user is initially inactive, the seed set is activated (influenced)
- When the influence from the set of active friends exceeds a threshold for a user v, the user activates
Influence is used as a proxy for adoption
§D. Kempe, J. Kleinberg, and E . Tardos. Maximizing the spread of influence through a social network. In KDD’03. ́�
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Influence ⇏ Adoption Observation: Only a subset of influenced users
actually adopt the marketed product
Influenced Adopt
Awareness/information spreads in an epidemic-like manner while adoption depends on factors such as product quality and price§
§S. Kalish. A new product adoption model with price, advertising, and uncertainty. Management Science, 31(12), 1985.
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Influence ⇏ Adoption Moreover we found that there exist users who help
in information propagation without actually adoption the product – tattlers.
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Our Model (LT-C)
Model Parameters• A is the set of active friends
• fv(A) is the activation function
• ru,i is the (predicted) rating for product i given by user u
• αv is the probability of user v adopting the product
• βv is the probability of user v promoting the product
User vActive Friends
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Maximizing Product Adoption Problem: Given a social network and product
ratings, find k users such that by targeting them the expected spread (expected number of adopters) under the LT-C model is maximized
Problem is NP-hard The spread function is monotone and submodular
yielding a 1-1/e approximation to the optimal using a greedy approach
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Evaluation• Data and Parameters• Key Findings
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Data
Number of nodes 13K 6040 1892Number of edges 192.4K 209K* 25.4KNumber of edges with non-zero weight 75.7K 154K 15.7KAverage degree 14.8 34.6 13.4Number of movies / artists 25K 3706 17.6KNumber of ratings 1.84M 1M 259K
*Movielens does not have an explicit social graph and we infer it from the ratings log, based on Jaccard similarity – in a recommender system, information/influence flows indirectly via recommendations.
• Flixster dataset has 2.3M special ratings, of which 730K ratings are “want to see it” and 1.6M are “not interested”
• last.fm has “loved” and “banned” songs
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Evaluation• Data and Parameters• Key Findings
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Spread Estimates
Our model (LT-C) better predicts spread for all datasets
Flixster
MovieLens
Last.fm
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Spread depends on product quality
Better quality products have better coverage
Classical LT model on theother hand predicts equalcoverage for all products
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Different seeds for different products
FlixsterMovieLens
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Key Takeaways Only a fraction of users who are influenced do
adopt the product The influence of an adopter on her friends is a
function of the adopter’s experience with the product, in addition to propagation probability
Non-adopters can play a role of “information bridges” helping in spreading the influence/information, and thus adoption by other users
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Thanks !!!