Abstract: A method of operating a messaging platform, including: obtaining, for a first profile, a first and a second topic of interest, a first intra-profile (IP) weight for the first topic of interest, and a second IP weight for the second topic of interest; obtaining a first plurality of trending entities for the first topic and a first plurality of intra-topic (IT) weights for the first plurality of trending entities; obtaining a second plurality of trending entities for the second topic and a second plurality of IT weights for the second plurality of trending entities; selecting a subset of the first plurality of trending entities and the second plurality of trending entities based on the first IP weight, the second IP weight, the first plurality of IT weights, and the second plurality of IT weights; and sending content associated with the subset for display to a user of the first profile.

Abstract: A method of operating a messaging platform, including: obtaining, for a first profile of the messaging platform, a trending entity within a first topic of interest to the first profile; accessing a first plurality of messages classified as the first topic of interest; retrieving a subset of the first plurality of messages corresponding to the trending entity; and sending content associated with the subset for display to a user of the first profile.

Abstract: A method of operating a messaging platform, including: obtaining, for a first profile of the messaging platform, a trending entity within a first topic of interest to the first profile; accessing a first plurality of messages classified as the first topic of interest; retrieving a subset of the first plurality of messages corresponding to the trending entity; and sending content associated with the subset for display to a user of the first profile.

Abstract: A method of operating a messaging platform, including: obtaining, for a first profile, a first and a second topic of interest, a first intra-profile (IP) weight for the first topic of interest, and a second IP weight for the second topic of interest; obtaining a first plurality of trending entities for the first topic and a first plurality of intra-topic (IT) weights for the first plurality of trending entities; obtaining a second plurality of trending entities for the second topic and a second plurality of IT weights for the second plurality of trending entities; selecting a subset of the first plurality of trending entities and the second plurality of trending entities based on the first IP weight, the second IP weight, the first plurality of IT weights, and the second plurality of IT weights; and sending content associated with the subset for display to a user of the first profile.

Abstract: One embodiment identifies a set of network resources relating to a search query; determines one or more sets of query suggestions for one or more network resources from the set of network resources, respectively, wherein each one of the one or more sets of query suggestions is related to a corresponding one of the one or more network resources; and provides the one or more network resources and the one or more sets of query suggestions in response to the search query, wherein each one of the one or more sets of query suggestions is provided in association with its corresponding one of the one or more network resources.

Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.

Abstract: One embodiment identifies a set of network resources relating to a search query; determines one or more sets of query suggestions for one or more network resources from the set of network resources, respectively, wherein each one of the one or more sets of query suggestions is related to a corresponding one of the one or more network resources; and provides the one or more network resources and the one or more sets of query suggestions in response to the search query, wherein each one of the one or more sets of query suggestions is provided in association with its corresponding one of the one or more network resources.

Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.

Abstract: A current query model is constructed representing a first histogram of a first set of counts corresponding to a first set of search queries and a current time interval. One or more historical query models are constructed, each uniquely representing a different second histogram of a different second set of counts corresponding to a different second set of search queries and a different historical time interval. A third search query is received. A first generation probability is calculated for the third search query with respect to the current query model. One or more second generation probabilities are calculated for the third search query with respect to the historical query models. A ratio is calculated based on the first generation probability and the second generation probabilities. The third search query is identified as corresponding to an emerging event if the ratio satisfies a predetermined threshold requirement.