BACKGROUND

In related art systems, when a recommendation of a media asset is made based on a present positive reaction by a user to a presently playing media asset, the recommendation is based purely on attributes of the media asset itself. Thus, if a user is reacting to a media asset for reasons unrelated to the user's present enjoyment of the media asset (e.g., because it evokes a pleasant memory), and the user in fact does not particularly enjoy the media asset itself, the related art will nonetheless recommend, to the user, media assets that share attributes with the media asset that the user is unlikely to be interested in listening to.

SUMMARY

Systems and methods are provided herein for detecting a reaction by a user to a media asset to which the user previously reacted at an earlier time, and recommending a second media asset to the user based on other media assets consumed by the user during a range of times adjacent to the earlier time. For example, the user might be driving while listening to “Purple Haze” by Jimi Hendrix. A media guidance application may receive an input from a microphone indicating that the user reacted to the song. Further, the media guidance application may determine that the microphone captured the user laughing and that the laugh was loud enough to be considered a significant reaction from the user. The media guidance application may then look for an entry in the database that was recorded at an earlier time and that indicates that the user previously reacted when listening to “Purple Haze,” and may determine that the user's previous reaction happened during his prom night and that “Hey Joe” by Jimi Hendrix played that night, after the playback of “Purple Haze.” The systems and methods may then recommend to the user that he listen to “Hey Joe” in order to better recreate the emotional environment that the user was experiencing during his prom night.

In some aspects, the media guidance application may detect that a user consumed a media asset at a first time. For example, the media guidance application may determine that the user listened to “Purple Haze” by Jimi Hendrix. In some embodiments, the media guidance application may use a plurality of sensors (e.g., a microphone, a camera), to detect that the user is consuming a media asset. If the media guidance application detects an input from the sensors (e.g., the microphone detects a noise, the camera detects movement on the display), the media guidance application may query each of the devices available to the user to determine that the input from the sensor is due to a media asset being consumed and not due to ambient noise (e.g., a person speaking, a passing vehicle is blasting music).

In some embodiments, the media guidance application may, while detecting the first reaction by the user to the media asset while the media asset was consumed by the user at the first time, receive a communication, by the user, that corresponds to the media asset, at a third time that is later than the first time. For example, the media guidance application may receive the user's chat history. Further, the media guidance application may decompose the communication into a plurality of segments. For example, the media guidance application may decompose the chat history into sentences. Even further, the media guidance application may detect the first reaction based on a segment from the plurality of segments. For example, the media guidance application may detect that the user, in one of the sentences, mentioned that he really enjoyed listening to “Purple Haze.”

In some embodiments, the media guidance application may detect a first reaction by the user to the media asset while the media asset was consumed by the user at the first time. For example, the media guidance application may detect an input from a microphone indicating a reaction from the user.

In some embodiments, the media guidance application may, in response to detecting the first reaction, determine a type of the first reaction. For example, the media guidance application may determine that the microphone registered the user laughing.

In some embodiments, the media guidance application may, while determining the type of the first reaction, determine a sensor type based on a sensor used to detect the first reaction. For example, the media guidance application may determine the sensor type as heartbeat because the sensor to detect the reaction was a heartbeat monitor. Further, the media guidance application may determine whether the sensor type corresponds to one reaction type, or to a plurality of candidate reaction types. For example, the media guidance application may determine that the heartbeat only corresponds to a heartbeat reaction while an input coming from a microphone can indicate laughing, screaming, or speech. Even further, the media guidance application may, in response to determining that the sensor type corresponds to one reaction type, determine the type of the first reaction based on the sensor type. For example, the media guidance application may determine that the sensor type is a heartbeat and may conclude that the reaction type is a heart rate reaction.

In some embodiments, the media guidance application may, in response to determining that the sensor type corresponds to a plurality of candidate reaction types, retrieve respective characteristics that correspond to each candidate reaction type of the plurality of candidate reaction types. For example, the media guidance application may determine that the sensor is a microphone that can indicate that the user is speaking, screaming, or laughing. Further, the media guidance application may compare characteristics of the first reaction to each respective characteristic of each candidate reaction type to determine a respective amount of matching characteristics for each respective candidate reaction type. For example, the media guidance application may compare the characteristics of the first reaction to the characteristics of samples of the user speaking, screaming and laughing. Even further, the media guidance application may determine the type of the first reaction to be the respective candidate reaction type that had the respective amount of matching characteristics that was a highest respective amount. For example, the media guidance application may conclude that the first reaction matches the laughing sample the best, and thus the media guidance application may determine the reaction to be laughing.

In some embodiments, the media guidance application may, in response to determining that the sensor type corresponds to a plurality of candidate reaction types, detect, using a different sensor from the sensor used to detect the first reaction, a third reaction by the user to the media asset while the media asset was consumed by the user at the first time. For example, the media guidance application may receive an input from a heart rate monitor. Further, the media guidance application may determine a sensor type of the different sensor. For example, the media guidance application may determine the sensor type as heartbeat because the sensor to detect the reaction was a heartbeat monitor. Even further, the media guidance application may determine the type of the first reaction based on the sensor type of the different sensor. For example, the media guidance application may determine, based on the microphone input and the heart rate monitor input, that the reaction is laughing.

In some embodiments, the media guidance application may convert a magnitude of the first reaction to a converted magnitude on a scale that corresponds to the type. For example, the media guidance application may convert the analog input of the sensor to a decibel scale to represent the loudness of the laugh.

In some embodiments, the media guidance application may, while converting the magnitude of the first reaction to the converted magnitude, retrieve the scale that corresponds to the type. For example, the media guidance application may, while converting the analog input from a microphone to an amplitude scale, retrieve the decibel scale and the level of analog input corresponding to a 0 decibel sound. Further, the media guidance application may determine whether a third reaction by the user to the media asset at a third time that is earlier than the first time and different from the second time, where the third reaction had a respective converted magnitude exceed a respective threshold, occurred. For example, the media guidance application may determine that the user was already laughing even before the user started listening to “Purple Haze.” Even further, the media guidance application may, in response to determining that the third reaction occurred, adjust the scale based on the third reaction, and may determine the converted magnitude of the first reaction based on the first reaction and the adjusted scale. For example, the media guidance application may adjust the scale for the loudness of the laugh to be measured at—the laugh has to be louder to be considered a sufficient reaction from the user.

In some embodiments, the media guidance application may then determine whether the converted magnitude is greater than a threshold value on the scale that corresponds to the type. For example, the media guidance application may determine that the laugh was loud enough to be considered a substantial reaction from the user.

In some embodiments, the media guidance application may, in response to determining that the converted magnitude is greater than the threshold value on the scale that corresponds to the type, search a database for an entry indicating a second reaction, by the user, to the media asset, at a second time that is earlier than the first time. For example, the media guidance application may search for a time in the past when the user reacted while listening to “Purple Haze.”

In some embodiments, the media guidance application may, in response to determining that the converted magnitude is greater than the threshold value on the scale that corresponds to the type, store the first reaction in a data structure. For example, the media guidance application may store the characteristics of the user's laugh in a database. Further, the media guidance application may store the first time in the data structure. For example, the media guidance application may store the time at which the user laughed in the database. Even further, the media guidance application may store second identities of a second plurality of media assets consumed by the user in the data structure during a predetermined range of time adjacent to the first time. For example, the media guidance application may store in the database the identity of the song “Little Wing” by Jimi Hendrix, because it was consumed two minutes before the playback of “Purple Haze.”

In some embodiments, the media guidance application may detect that the user consumed the media asset at a third time that is later than the first time. For example, the media guidance application may detect that the user was listening to “Purple Haze” one week later after he was listening to it in the car. Further, the media guidance application may detect a third reaction by the user to the media asset, while the media asset was consumed by the user at the third time. For example, the media guidance application may determine that the user reacted to the playback of “Purple Haze” one week after the user was listening to “Purple Haze” in the car. Even further, the media guidance application may, in response to detecting the third reaction, determine a type of the third reaction, convert a magnitude of the third reaction to a second converted magnitude on a scale that corresponds to the second type, and determine whether the second converted magnitude is greater than a threshold value on the second scale that corresponds to the second type. For example, the media guidance application may detect that the user laughed loudly enough while listening to “Purple Haze.”

In some embodiments, the media guidance application may, in response to determining that the second converted magnitude is greater than the second threshold value on the second scale that corresponds to the second type, recommend a third media asset, to the user, based on the second identities of each of the second plurality of media assets. For example, the media guidance application may recommend that the user listen to the song “Little Wing” by song artist Jimi Hendrix, because “Little Wing” was played during the user's prom night after “Purple Haze.”

In some embodiments, the media guidance application may, while recommending the third media asset to the user, based on the second identities of each of the second plurality of media assets, determine a type of the second reaction. For example, the media guidance application may determine that the second reaction is laughter. Further, the media guidance application may determine a type of the first reaction. Even further, the media guidance application may recommend the third media asset, to the user, based on the identities of each of the plurality of media assets, the second identities of each of the second plurality of media asset, the type of the first reaction, and the type of the second reaction. For example, the media guidance application may determine that the user was laughing both times while listening to “Purple Haze”. Further, the media guidance application may recommend the song “Hey Joe” by the song artist Jimi Hendrix, to the user because “Hey Joe” was played after the playback of “Purple Haze” during the prom night.

In some embodiments, the media guidance application may, in response to finding, based on the searching, the entry indicating the second reaction by the user to the media asset at the second time, retrieve, based on information of the entry, identities of each of a plurality of media assets consumed by the user during a predetermined range of time adjacent to the second time. For example, the media guidance application may determine that the last time the user reacted, while listening to “Purple Haze,” was during his prom night. The media guidance application may then retrieve the playlist that was playing at the user's prom night.

In some embodiments, the media guidance application may retrieve a plurality of media assets that correspond to the identities of each of the plurality of media assets. Further, the media guidance application may generate a playlist of media assets using the plurality of media assets. For example, the media guidance application may generate the playlist based on the songs that played during the prom night, when “Purple Haze” was played. Even further, the media guidance application may play back the plurality of media assets in the playlist. For example, the media guidance application may play back the playlist that played during the prom night, when “Purple Haze” was played.

In some embodiments, the media guidance application may recommend a second media asset to the user, based on the identities of each of the plurality of media assets. For example, the media guidance application may recommend “Hey Joe” by Jimi Hendrix, which was playing after the playback of “Purple Haze” during the user's prom night.

