Social content suggestions based on connections
First Claim
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1. A method for recommending content in a social network, comprising:
- logging a set of actions of a user;
categorizing the set of actions into interest categories;
weighting the interest categories to generate weighted interest categories, the weighting comprising;
for a first interest category associated with a first subset of the set of actions;
identifying one or more types of actions associated with the first interest category, respective types of actions associated with an action weight, the identifying comprising;
identifying a first type of action corresponding to a search action, a second type of action corresponding to a subscribe action, and a third type of action corresponding to a click action, the first type of action, the second type of action and the third type of action associated with the first interest category, the first type of action having a first action weight, the second type of action having a second action weight, and the third type of action having a third action weight; and
assigning a first weight to the first interest category based on the one or more types of actions associated with the first interest category and an action weight associated with each of the one or more types of actions;
calculating an interest social index for the user as a function of the weighted interest categories; and
providing, on a social page associated with the social network, and based on the interest social index, a first indication of a first suggested topic and a first value corresponding to a likelihood of one or more users in the social network engaging the first suggested topic and a second indication of a second suggested topic and a second value corresponding to a likelihood of one or more users in the social network engaging the second suggested topic.
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Abstract
A system and method for recommending content to a user in a social network, including: logging user activity for the user in the social network; categorizing the user activity across all the user'"'"'s networks, wherein each category is assigned a score based on relevance to the user; assigning weights to the user activities; calculating a social index score as a function of the weighted user activity categories; logging user content into categories; scoring the user content; and generating a content social index by weighting the content scores.
35 Citations
20 Claims
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1. A method for recommending content in a social network, comprising:
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logging a set of actions of a user; categorizing the set of actions into interest categories; weighting the interest categories to generate weighted interest categories, the weighting comprising; for a first interest category associated with a first subset of the set of actions; identifying one or more types of actions associated with the first interest category, respective types of actions associated with an action weight, the identifying comprising; identifying a first type of action corresponding to a search action, a second type of action corresponding to a subscribe action, and a third type of action corresponding to a click action, the first type of action, the second type of action and the third type of action associated with the first interest category, the first type of action having a first action weight, the second type of action having a second action weight, and the third type of action having a third action weight; and assigning a first weight to the first interest category based on the one or more types of actions associated with the first interest category and an action weight associated with each of the one or more types of actions; calculating an interest social index for the user as a function of the weighted interest categories; and providing, on a social page associated with the social network, and based on the interest social index, a first indication of a first suggested topic and a first value corresponding to a likelihood of one or more users in the social network engaging the first suggested topic and a second indication of a second suggested topic and a second value corresponding to a likelihood of one or more users in the social network engaging the second suggested topic. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. An information processing system for recommending content in a social network, said information processing system comprising:
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a processor; and memory comprising computer-executable instructions that when executed by the processor perform a method, comprising; for a user; logging a set of actions; categorizing the set of actions into interest categories; weighting the interest categories to generate weighted interest categories, the weighting comprising; for a first interest category associated with a first subset of the set of actions;
identifying one or more types of actions associated with the first interest category, respective types of actions associated with an action weight, the identifying comprising;
identifying a first type of action corresponding to a search action, a subscribe action or a click action, a second type of action, and a third type of action, the first type of action, the second type of action and the third type of action associated with the first interest category, the first type of action having a first action weight, the second type of action having a second action weight, and the third type of action having a third action weight; and
assigning a first weight to the first interest category based on the one or more types of actions associated with the first interest category and an action weight associated with each of the one or more types of actions; andcalculating an interest social index for the user as a function of the weighted interest categories; for a second user within the social network of the user; logging a second set of actions; categorizing the second set of actions into a second set of interest categories; weighting the second set of interest categories to generate a second set of weighted interest categories; and calculating an interest social index for the second user as a function of the weighted interest categories; and calculating an influencer social index for the user based on the interest social index for the second user. - View Dependent Claims (15, 16, 17, 18, 19)
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20. A computer program product comprising a non-transitory computer readable storage medium with computer-executable instructions stored thereon, said computer-executable instructions comprising:
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logging a set of actions of a user; categorizing the set of actions into interest categories; weighting the interest categories to generate weighted interest categories, the weighting comprising; for a first interest category associated with a first subset of the set of actions; identifying one or more types of actions associated with the first interest category, respective types of actions associated with an action weight, the identifying comprising; identifying a first type of action, a second type of action, and a third type of action associated with the first interest category, the first type of action having a first action weight, the second type of action having a second action weight, and the third type of action having a third action weight; and assigning a first weight to the first interest category based on the one or more types of actions associated with the first interest category and an action weight associated with each of the one or more types of actions; calculating an interest social index for the user as a function of the weighted interest categories; and providing, on a social page, and based on the interest social index, a first indication of a first suggested topic and a first value corresponding to a likelihood of one or more users engaging the first suggested topic and a second indication of a second suggested topic and a second value corresponding to a likelihood of one or more users engaging the second suggested topic.
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Specification