The present disclosure discloses a method, apparatus, and server for information recommendation. Search behavior data, browsing behavior data, and click behavior data on recommended content of a specified user in a forum are acquired. A preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on recommended content is performed respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result. Distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result are performed according to weights to obtain recommended content to be recommended to the specified user. Search behavior data, browsing behavior data, and click behavior data on recommended content are taken into comprehensive consideration, data used in recommendation is enriched, and accuracy of recommendation is improved.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information recommendation method, comprising: acquiring search behavior data, browsing behavior data, and click behavior data on recommended content of a specified user in a forum; performing a preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on the recommended content respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result; and performing distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result according to weights, to obtain recommending content to be recommended to the specified user. 2. The method according to claim 1, wherein the step of performing the preprocessing on the search behavior data to obtain the first recommendation result comprises: filtering out noise data in the search behavior data to obtain noise-cancelled data, wherein the noise-cancelled data comprises a query string input in the forum by the specified user; counting a number of clicks triggered by each query string Qi on a post Ti; calculating, according to the number of clicks triggered by each query string Qi on the post Ti, a click probability wi triggered by each query string on the post Ti; establishing, according to the click probability wi triggered by each query string on the post Ti, a query vector for triggering a click on the post Ti, wherein the query vector is <w1, w2, . . . , wi, . . . , wn>; and calculating, according to a query vector established for each post, a relevance degree between any two posts, to obtain the first recommendation result. 3. The method according to claim 1, wherein the step of performing the preprocessing on the browsing behavior data to obtain the second recommendation result comprises: acquiring a post browsed by the specified user in the forum by analyzing the browsing behavior data; categorizing, according to a section containing the browsed post, browsing behavior of the specified user into at least one parent subject segment; calculating text similarities between titles of posts in each parent subject segment to obtain boundaries between child subject segments in each parent subject segment; according to the boundaries between the child subject segments of a corresponding parent subject segment, categorizing the corresponding parent subject segment to obtain at least one child subject segment; establishing, according to each child subject segment, a browsing behavior matrix of the specified user, wherein the browsing behavior matrix comprises: a child subject and the number of appearing times that posts in the forum appear in the child subject; and performing a relevance degree calculation on the browsing behavior matrix, to obtain the second recommendation result. 4. The method according to claim 1, wherein the step of performing the preprocessing on the click behavior data to obtain the third recommendation result comprises: analyzing the click behavior data to obtain related posts of each post in the forum and to obtain the number of times that each related post is clicked; and re-sorting, according to the number of times that each related post is clicked and a relationship between a click time when the related post is clicked and a current time, the related posts in the forum, so that a related post, having a most number of clicks and having a difference value between the click time and the current time within a preset range, is sorted in a front of a queue, to obtain the third recommendation result. 5. The method according to claim 1, wherein the step of performing distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result according to weights to obtain the recommending content to be recommended to the specified user comprises: calculating respectively a first average probability, a second average probability, and a third average probability that related posts of each post in the forum appear in the first recommendation result, the second recommendation result, and the third recommendation result; determining respectively, according to the first average probability, the second average probability, and the third average probability, a weight of the first recommendation result, a weight of the second recommendation result, and a weight of the third recommendation result; and performing, based on the weight of the first recommendation result, the weight of the second recommendation result, and the weight of the third recommendation result, distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain the recommending content to be recommended to the specified user; or performing, according to a preset first weight of the first recommendation result, a preset second weight of the second recommendation result, and a preset third weight of the third recommendation result, distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain the recommending content to be recommended to the specified user. 6. An information recommendation apparatus, comprising; an acquisition module, configured to acquire search behavior data browsing behavior data, and click behavior data on recommended content of a specified user in a forum; a preprocessing module, configured to perform a preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on the recommended content respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result; and an integration module, configured to perform distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain recommending content to be recommended to the specified user-. 7. The apparatus according to claim 6, wherein the preprocessing module comprises: a filtering unit, configured to filter out noise data in the search behavior data to obtain noise-cancelled data, wherein the noise-cancelled data comprises a query string input in the forum by the specified user; a count unit, configured to count a number of clicks triggered by each query string Qi on a post Ti; a first calculation unit, configured to calculate, according to the number of clicks triggered by each query string Qi on the post Ti, a click probability wi triggered by each query string on the post Ti; a first establishing unit, configured to establish, according to the click probability wi triggered by each query string on the post Ti, a query vector for triggering a click on the post Ti, wherein the query vector is <w1, w2, . . . , wi, . . . , wn>; and a second calculation unit, configured to calculate, according to a query vector established for each post, a relevance degree between any two posts, to obtain the first recommendation result. 8. The apparatus according to claim 6, wherein the preprocessing module comprises: an acquisition unit, configured to acquire a post browsed by the specified user in the forum by analyzing the browsing behavior data; a first categorization unit, configured to categorize, according to a section containing the browsed post, browsing behavior of the specified user into at least one parent subject segment; a third calculation unit, configured to calculate text similarities between titles of posts in each parent subject segment, to obtain boundaries between child subject segments in each parent subject segment; a second categorization unit, configured to categorize a corresponding parent subject segment according to the boundaries between the child subject segments of the corresponding parent subject segment, to obtain at least one child subject segment; a second establishing unit, configured to establish, according to each child subject segment, a browsing behavior matrix of the specified user, wherein the browsing behavior matrix comprises: a child subject and the number of appearing times that posts in the forum appear in the child subject; and a fourth calculation unit, configured to perform relevance degree calculation on the browsing behavior matrix, to obtain the second recommendation result. 9. The apparatus according to claim 6, wherein the preprocessing module comprises: an analysis unit, configured to analyze the click behavior data, to obtain related posts of each post in the forum and to obtain the number of times that each related post is clicked; and a re-sorting unit, configured to re-sort, according to the number of times that each related post is clicked and a relationship between a click time when the related post is clicked and a current time, the related posts in the forum, so that a related post, having a most number of clicks and having a difference value between the click time and the current time within a preset range, is sorted in the front of a queue, to obtain the third recommendation result. 10. The apparatus according to claim 6, wherein the integration module comprises: a calculation unit, configured to calculate respectively a first average probability, a second average probability, and a third average probability that related posts of each post in the forum appear in the first recommendation result, the second recommendation result, and the third recommendation, result; and a determination unit, configured to determine respectively, according to the first average probability, the second average probability, and the third average probability, a weight of the first recommendation result, a weight of the second recommendation result, and a weight of the third recommendation result; and a first integration unit, configured to perform, based on the weight of the first recommendation result, the weight of the second recommendation result, and the weight of the third recommendation result, distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain the recommending content to be recommended to the specified user; or a second integration unit, configured to perform, according to a preset first weight of the first recommendation result, a preset second weight of the second recommendation result, and a preset third weight of the third recommendation result, distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain the recommending content to be recommended to the specified user. 11. A server comprising: one or more processors; and a non-transitory computer-readable storage medium having one or more programs stored thereon, wherein the one or more programs are executed by the one or more processors and comprise instructions for performing following operations: acquiring search behavior data, browsing behavior data, and click behavior data on recommended content of a specified user in a forum; performing a preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on the recommended content respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result; and performing distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result according to weights, to obtain recommending content to be recommended to the specified user. 12. The server according to claim 11, further comprising instructions for performing following operations: filtering out noise data in the search behavior data to obtain noise-cancelled data, wherein the noise-cancelled data comprises a query string input in the forum by the specified user; counting a number of clicks triggered by each query string Qi on a post Ti; calculating, according to the number of clicks triggered by each query string Qi on the post Ti, a click probability wi triggered by each query string on the post Ti; establishing, according to the click probability wi triggered by each query string on the post Ti, a query vector for triggering a click on the post Ti, wherein the query vector is <w1, w2, . . . , wi, . . . , wn>; and calculating, according to a query vector established for each post, a relevance degree between any two posts, to obtain the first recommendation result. 13. The server according to claim 11, further comprising instructions for performing following operations: acquiring a post browsed by the specified user in the forum by analyzing the browsing behavior data; categorizing, according to a section containing the browsed post, browsing behavior of the specified user into at least one parent subject segment; calculating text similarities between titles of posts in each parent subject segment, to obtain boundaries between child subject segments in each parent subject segment; according to the boundaries between the child subject segments of a corresponding parent subject segment, categorizing the corresponding parent subject segment to obtain at least one child subject segment; establishing, according to each child subject segment, a browsing behavior matrix of the specified user, wherein the browsing behavior matrix comprises: a child subject and the number of appearing times that posts in the forum appear in the child subject; and performing a relevance degree calculation on the browsing behavior matrix, to obtain the second recommendation result. 14. The server according to claim 11, further comprising instructions for performing following operations: analyzing the click behavior data, to obtain related posts of each post in the forum and to obtain the number of times that each related post is clicked; and re-sorting, according to the number of times that each related post is clicked and a relationship between the click time when the related post is clicked and a current time, the related posts in the forum, so that a related post, having a most number of clicks and having a difference value between the click time and the current time within a preset range, is sorted in the front of a queue, to obtain the third recommendation result. 15. The server according to claim 11, further comprising instructions for performing following operations: calculating respectively a first average probability, a second average probability, and a third average probability that related posts of each post in the forum appear in the first recommendation result, the second recommendation result, and the third recommendation result; determining respectively, according to the first average probability, the second average probability, and the third average probability, a weight of the first recommendation result, a weight of the second recommendation result, and a weight of the third recommendation result; and performing, based on the weight of the first recommendation result, the weight of the second recommendation result, and the weight of the third recommendation result, distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain the recommending content to be recommended to the specified user; or performing, according to a preset first weight of the first recommendation result, a preset second weight of the second recommendation result, and a preset third weight of the third recommendation result, distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result, to obtain the recommending content to be recommended to the specified user.